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4,000
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'pagsaka', 'sa', 'presyo', 'sa', 'lana', ',', 'epektibo', 'sa', 'Martes', ',', 'Hunyo', '27', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,001
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MOTORISTA', ',', 'PATAY', 'HUMAN', 'MALIGSAN', 'ANG', 'ULO', 'SA', '10-WHEELER', 'DUMP', 'TRUCK', 'Patay', 'ang', 'usa', 'ka', 'motorista', 'sa', 'usa', 'ka', 'vehicular', 'accident', 'ang', 'nahitabo', 'sa', 'Barangay', 'Caidiocan', 'sa', 'Valencia', 'mga', 'alas-6:20', 'sa', 'gabii', 'niadtong', 'Sabado', ',', 'Hunyo', '24,2023.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'Ruperto', 'Solamillo', 'Ausejo', 'Jr.', ',', '46', 'anyos', ',', 'ulitawo', ',', 'ug', 'lumolupyo', 'sa', 'Barangay', 'Pulang', 'Bato', 'sa', 'naasoy', 'nga', 'lungsod.', 'Sumala', 'pa', 'sa', 'imbestigasyon', 'sa', 'kapulisan', 'sa', 'Valencia', ',', 'nagbiyahe', 'ang', 'duha', 'sa', 'managlahing', 'direksyon.', 'Pag-abot', 'sa', 'nahisgutang', 'lugar', ',', 'ni-overtake', 'si', 'Ausejo', 'sa', 'truck', 'ug', 'nawad-an', 'og', 'kontrol', 'sa', 'gimanehong', 'motorsiklo', 'nga', 'niresulta', 'sa', 'iyang', 'pagkatumba', 'sa', 'karsada.', 'Tungod', 'niini', ',', 'aksidenteng', 'naligsan', 'sa', '10-wheeler', 'dump', 'truck', 'ang', 'ulo', 'sa', 'biktima', 'hinungdan', 'sa', 'iyang', 'hinanaling', 'kamatayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
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|
cebuaner
|
4,002
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['9', 'KA', 'MAYOR', 'SA', 'NEGOR', ',', 'NANAWAGAN', 'NGA', 'I-POSTPONE', 'ANG', 'BARANGAY', ',', 'SK', 'ELECTION', 'Nipadayag', 'og', 'suporta', 'ang', 'siyam', 'ka', 'mga', 'mayor', 'sa', 'Negros', 'Oriental', 'sa', 'sugyot', 'nga', 'i-postpone', 'ang', 'eleksyon', 'sa', 'Barangay', 'ug', 'Sangguniang', 'Kabataan', '(', 'SK', ')', 'sa', 'probinsya', 'nga', 'gitakda', 'karong', 'Oktubre.', 'Ilang', 'gisubli', 'ang', 'mga', 'kabalaka', 'bahin', 'sa', 'seguridad', 'lakip', 'na', 'ang', '"', 'deep-rooted', 'fear', '"', 'sa', 'mga', 'residente', 'sa', 'probinsya', 'human', 'sa', 'pagpatay', 'kang', 'Gov.', 'Roel', 'Degamo.', 'Lakip', 'sa', 'mga', 'nipirma', 'sa', 'manipesto', 'ang', 'biyuda', 'ni', 'Degamo', 'nga', 'si', 'Janice', 'Degamo', 'sa', 'Pamplona', ',', 'iyang', 'pag-umangkon', 'nga', 'si', 'Fritz', 'Diaz', 'sa', 'Siaton', ',', 'ug', 'mga', 'kaalyado', 'nga', 'sila', 'si', 'Felipe', 'Remollo', 'sa', 'Dumaguete', 'City', ',', 'Galicano', 'Truita', 'sa', 'Dauin', ',', 'Mel', 'Nick', 'Logronio', 'sa', 'San', 'Jose', ',', 'Eniego', 'Jabagat', 'sa', 'Bindoy', ',', 'Dennis', 'Amancio', 'sa', 'Ayungon', ',', 'Filomeno', 'Reyes', 'sa', 'Guihulngan', 'City', ',', 'ug', 'Susano', 'Ruperto', 'sa', 'Tayasan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 5, 0, 0, 0, 0, 0, 1, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 0, 1, 2, 0, 5, 0, 1, 2, 2, 0, 5, 6, 0, 1, 2, 0, 5, 0, 1, 2, 0, 5, 0, 1, 2, 0, 5, 6, 0, 0, 1, 2, 0, 5, 0]
|
cebuaner
|
4,003
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'SA', 'INDIA', ',', 'NIPUYO', 'OG', 'DUL-AN', '2', 'KA', 'TUIG', 'SA', 'USA', 'KA', '5-STAR', 'HOTEL', 'NGA', 'WALA’Y', 'BAYAD-BAYAD', 'Usa', 'ka', 'lalaki', 'ang', 'giimbestigaran', 'sa', 'kapulisan', 'sa', 'India', 'human', 'giingong', 'nagpuyo', 'kini', 'og', 'halos', 'duha', 'ka', 'tuig', 'sa', 'usa', 'ka', 'five-star', 'hotel', 'sa', 'New', 'Delhi', 'nga', 'wala'y', 'bayad-bayad.', 'Nag-book', 'og', 'kwarto', 'si', 'Ankush', 'Dutta', 'sa', 'Roseate', 'House', 'hotel', 'niadtong', 'May', '30', ',', '2019', 'ug', 'mo-check', 'out', 'unta', 'dayon', 'sa', 'sunod', 'adlaw.', 'Apan', 'na-extend', 'kini', 'og', '603', 'ka', 'mga', 'adlaw', 'hangtod', 'sa', 'iyang', 'pagbiya', 'niadtong', 'Jan.', '22', ',', '2021', ',', 'ug', 'gibiyaan', 'ang', 'iyang', '$', '70,000', 'nga', 'balayran.', 'Nipasaka', 'og', 'reklamo', 'sa', 'kapulisan', 'ang', 'hotel', 'managers', 'kontra', 'sa', 'pipila', 'ka', 'mga', 'empleyado', 'niini', 'tungod', 'sa', '"', 'conspiracy', ',', 'forgery', 'and', 'cheating.', '"', 'Dugang', 'pa', ',', 'giingong', 'gisuholan', 'ni', 'Dutta', 'ang', 'pipila', 'ka', 'mga', 'kawani', 'sa', 'hotel', 'aron', 'pagmaniobra', 'sa', 'in-house', 'software', 'systems', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,004
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'hulagyway', 'ni', 'President', 'Ferdinand', 'Marcos', 'Jr.', 'uban', 'sa', 'pipila', 'ka', 'mga', 'lokal', 'nga', 'opisyales', 'sa', 'probinsya', 'sa', 'Negros', 'Oriental.', 'Nakigpulong', 'ang', 'presidente', 'aron', 'hisgutan', 'ang', 'mga', 'isyu', 'bahin', 'sa', 'kalambuan', 'sa', 'probinsya.', 'Lakip', 'sa', 'mga', 'nakigpulong', 'ni', 'Marcos', 'mao', 'sila', 'si', 'San', 'Jose', 'Mayor', 'Mel', 'Nick', 'Logronio', ',', 'Pamplona', 'Mayor', 'Janice', 'Degamo', ',', 'ug', 'Siaton', 'Mayor', 'Fritz', 'Diaz', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 6, 0, 1, 2, 2, 0, 5, 0, 1, 2, 0, 0, 5, 0, 1, 2, 0]
|
cebuaner
|
4,005
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['STATE', 'OF', 'CALAMITY', 'SA', 'TIBUOK', 'PILIPINAS', 'TUNGOD', 'SA', 'ASF', ',', 'GITUN-AN', 'Gitun-an', 'karon', 'sa', 'Department', 'of', 'Agriculture', '(', 'DA', ')', 'ang', 'pagsugyot', 'ngadto', 'ni', 'Pres.', 'Ferdinand', 'Marcos', 'Jr.', 'nga', 'magdeklarar', 'og', 'state', 'of', 'calamity', 'taliwala', 'sa', 'pagkaylap', 'sa', 'African', 'Swine', 'Fever', '(', 'ASF', ')', 'sa', 'nasud.', 'Sumala', 'pa', 'sa', 'tigpamaba', 'sa', 'DA', 'nga', 'si', 'Kristine', 'Evangelista', ',', 'nakigkoordinar', 'na', 'ang', 'ilang', 'ahensya', 'sa', 'Bureau', 'of', 'Animal', 'Industry', '(', 'BAI', ')', 'aron', 'pagpangita', 'og', 'mga', 'pamaagi', 'nga', 'matabangan', 'ang', 'mga', 'hog', 'raiser', 'nga', 'makakuha', 'og', 'ASF', 'vaccines.', 'Gisubli', 'sab', 'ni', 'Evangelista', 'nga', 'nagpatuman', 'na', 'sila', 'og', 'dugang', 'nga', 'mga', 'lakang', 'aron', 'mapunggan', 'ang', 'pagkuyanap', 'sa', 'swine', 'disease.', 'Matud', 'pa', 'ni', 'BAI', 'assistant', 'director', 'Arlene', 'Vytiaco', ',', 'plano', 'sab', 'sa', 'ilang', 'ahensya', 'ang', 'pagpalit', 'og', '600,000', 'doses', 'sa', 'ASF', 'vaccines', 'karong', 'tuiga', 'sa', 'dihang', 'maka-isyu', 'na', 'ang', 'Food', 'and', 'Drug', 'Administration', 'og', 'certificate', 'of', 'product', 'registration', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,006
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'HAYOP', 'SA', 'AMLAN', 'ZOO', ',', 'IBALHIN', 'SA', 'AKLAN', 'Dal-on', 'sa', 'usa', 'ka', 'private', 'farm', 'sa', 'Bukid', 'Tigayon', 'Kalibo', ',', 'Aklan', 'ang', 'mga', 'hayop', 'sa', 'Amlan', 'Nature', 'and', 'Adventure', 'Park.', 'Ang', 'tag-iya', 'sa', 'Kalibo', 'Ostrich', 'Farm', 'nga', 'si', 'Ramon', 'Dio', 'ang', 'mobalhin', 'sa', 'kapin', '80', 'ka', 'mga', 'hayop', 'sa', 'maong', 'farm', 'kaabag', 'ang', 'Department', 'of', 'Environment', 'and', 'Natural', 'Resources', '(', 'DENR', ')', '.', 'Nahibaloan', 'nga', 'ang', 'maong', 'farm', 'nagkulang', 'og', 'supply', 'sa', 'pagkaon', 'hinungdan', 'nga', 'ang', 'maong', 'mga', 'hayop', 'nagniwang', 'ug', 'nagkasakit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 6, 6, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,007
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['16', 'KA', 'BULAN', 'NGA', 'BATA', ',', 'PATAY', 'HUMAN', 'GIBIYAAN', 'OG', '8', 'KA', 'ADLAW', 'SA', 'INAHAN', 'ARON', 'MAGBAKASYON', 'Patay', 'na', 'nga', 'napalgan', 'ang', 'usa', 'ka', '16', 'ka', 'bulan', 'nga', 'batang', 'babayi', 'human', 'giingong', 'gibiyaan', 'siya', 'nga', 'nag-inusara', 'sa', 'iyang', 'inahan', 'sulod', 'sa', 'walo', 'ka', 'adlaw', 'samtang', 'kini', 'nagbakasyon.', 'Gidakop', 'sa', 'kapulisan', 'sa', 'Ohio', 'ang', 'inahan', 'sa', 'bata', 'nga', 'si', 'Kristel', 'Candelario', ',', '31', 'anyos.', 'Sumala', 'pa', 'sa', 'awtoridad', ',', 'napalgan', 'ni', 'Candelario', 'ang', 'iyang', 'anak', 'nga', 'wala', 'na'y', 'kinabuhi', 'sa', 'dihang', 'nipauli', 'siya', 'gikan', 'sa', 'Puerto', 'Rico.', 'Bisan', 'pa', 'man', 'wala', 'gipagawas', 'sa', 'medical', 'examiner', 'ang', 'hinungdan', 'sa', 'kamatayon', ',', 'gisubli', 'sa', 'imbestigador', 'nga', 'grabe', 'ka', 'dehydrated', 'ang', 'bata.', 'Matud', 'pa', 'sa', 'kapulisan', ',', 'gibutyag', 'sa', 'mga', 'silingan', 'nga', 'ang', 'lola', 'sa', 'bata', 'mao'y', 'kalagmitang', 'magbantay', 'niini', 'tungod', 'hilig', 'gyud', 'nga', 'mogawas', 'ang', 'inahan', 'niini.', 'Pasakaan', 'og', 'kasong', 'pagpatay', 'ug', 'multa', 'nga', '$', '1', 'milyones', 'si', 'Candelario', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
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|
cebuaner
|
4,008
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGROS', 'ORIENTAL', 'PROVINCIAL', 'HOSPITAL', ',', 'NASUPLAYAN', 'OG', 'P10-M', 'NGA', 'DUGANG', 'TAMBAL', 'Naabot', 'na', 'kagahapong', 'adlawa', 'ang', 'mga', 'stock', 'sa', 'medisina', 'nga', 'nagkantidad', 'og', 'P10', 'milyones', 'isip', 'paghatag', 'og', 'pagtagad', 'sa', 'suplay', 'sa', 'tambal', 'sa', 'ospital', 'ning', 'probinsya.', 'Dali', 'kining', 'gipapalit', 'sa', 'kagamhanan', 'sa', 'probinsya', 'aron', 'aduna'y', 'dugang', 'suplay', 'sa', 'tambal', 'ang', 'Negros', 'Oriental', 'Provincial', 'Hospital', 'ug', 'mahatag', 'kini', 'sa', 'mga', 'gakinahanglan', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,009
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'pa'y', 'nakadaog', 'sa', 'kapin', 'P292', 'milyones', 'nga', 'jackpot', 'prize', 'sa', 'Ultra', 'Lotto', '6', '/', '58.', 'Sumala', 'pa', 'sa', 'PCSO', ',', 'wala'y', 'nakakuha', 'sa', 'winning', 'combination', 'sa', 'draw', 'niini', 'niadtong', 'Martes', ',', 'Hunyo', '20', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,010
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GIINGONG', 'ILLEGAL', 'QUARRY', 'SA', 'NEGROS', 'ORIENTAL', ',', 'NADAKPAN', 'Nasikop', 'sa', 'Criminal', 'Investigation', 'and', 'Detection', 'Group', '(', 'CIDG-PNP', ')', 'ang', 'giingong', 'illegal', 'quarry', 'operation', 'nga', 'gikatahong', 'doul', 'sa', 'dakbayan', 'sa', 'Dumaguete.', 'Kini', 'human', 'gimandoan', 'ni', 'Negros', 'Oriental', 'Gov.', 'Manuel', '"', 'Chaco', '"', 'Sagarbarria', 'ang', 'kapulisan', 'sa', 'paglanat', 'sa', 'mga', 'illegal', 'quarry', 'operation.', 'Usa', 'kini', 'sa', 'mga', 'dagkong', 'polisiya', 'nga', 'ipatuman', 'sa', 'probinsya', 'aron', 'pagpreserbar', 'sa', 'kinaiyahan.', 'Ang', 'direktiba', 'pinaagi', 'sa', 'usa', 'ka', 'Memorandum', 'Order', 'nag-ingon', 'nga', '"', 'pursuant', 'to', 'the', 'Local', 'Government', 'Code', ',', 'the', 'provincial', 'Governor', 'has', 'the', 'exclusive', 'authority', 'to', 'issue', 'permit', 'to', 'extract', 'sand', ',', 'gravel', ',', 'and', 'other', 'quarry', 'resources.', '"', 'Masiguro', 'niini', 'nga', 'hugot', 'nga', 'masunod', 'sa', 'tanang', 'quarry', 'operators', 'ug', 'ilang', 'operasyon', 'sa', 'probinsya', 'ang', 'mga', 'lagda', 'ug', 'regulasyon', 'sa', 'quarry', 'permit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 6, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,011
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JOB', 'ORDER', 'UG', 'CONTRACT', 'OF', 'SERVICE', 'WORKERS', 'SA', 'DGTE', ',', 'MAKADAWAT', 'OG', '25KG', 'NGA', 'BUGAS', 'Makadawat', 'og', '25kg', 'nga', 'ayudang', 'bugas', 'ang', 'matag', 'usa', 'sa', '1,510', 'ka', 'mga', 'Contract', 'of', 'Service', 'ug', 'Job', 'Order', 'workers', 'sa', 'LGU-Dumaguete', 'karong', 'Miyerkules', 'ug', 'Huwebes', 'sa', 'City', 'Hall', 'Grounds.', 'Sa', 'usa', 'ka', 'memorandum', ',', 'gisiguro', 'sab', 'nga', 'magpadayon', 'ang', 'operasyon', 'sa', 'kagamhanang', 'syudad', 'ug', 'kahapsay', 'niini', 'atol', 'sa', 'naasoy', 'nga', 'mga', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,012
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PLDT', ',', 'NAGSUGYOT', 'OG', 'PAMAAGI', 'ARON', 'MAPAKUSGAN', 'ANG', 'CCTV', 'SA', 'DUMAGUETE', ';', 'POSIBLENG', 'MAGTAOD', 'SAB', 'OG', 'PUBLIC', 'WIFI', 'UG', 'TRAFFIC', 'LIGHTS', 'Gisugyot', 'sa', 'Philippine', 'Long', 'Distance', 'Incorporated', '(', 'PLDT', ')', 'uban', 'sa', 'Fortinet', 'ang', 'ilang', 'gi-bundle', 'nga', 'high', 'grade', 'equipment', 'uban', 'sa', 'ilang', 'lig-on', 'nga', 'network.', 'Maminusan', 'niini', 'ang', 'pagkawala', 'sa', 'feed', 'sa', 'mga', 'CCTV', 'camera', 'kun', 'ugaling', 'maputol', 'ang', 'mga', 'fiber', 'optic', 'cable', 'niini', 'tungod', 'sa', 'mga', 'wala', 'damhang', 'insidente.', 'Gipresentar', 'kini', 'nila', 'ngadto', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ug', 'sa', 'ubang', 'hingtungdan', 'nga', 'kawani', 'sa', 'LGU.', 'Kung', 'maaprobahan', 'ang', 'gisugyot', 'nga', 'solusyon', 'sa', 'Fortiner', 'SDWAN', ',', 'magsilbi', 'sab', 'kini', 'nga', 'pamaagi', 'aron', 'pag-install', 'og', 'public', 'wifi', 'ug', 'intelligent', 'traffic', 'lights', 'systems', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,013
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BABAYI', 'SA', 'ECUADOR', 'NGA', 'NAKAMATA', 'SULOD', 'SA', 'IYANG', 'LUNGON', ',', 'TINUOD', 'NA', 'NGA', 'PATAY', 'Tinuod', 'na', 'nga', 'namatay', 'ang', 'usa', 'ka', 'tigulang', 'nga', 'babayi', 'sa', 'Ecuador', 'kinsa', 'kaniadto', 'nakamata', 'sulod', 'sa', 'usa', 'ka', 'lungon', 'sa', 'iyang', 'kaugalingong', 'haya.', 'Sa', 'niaging', 'semana', ',', 'nag-viral', 'sa', 'social', 'media', 'ang', 'video', 'ni', 'Bella', 'Montoya', ',', '76', 'anyos', ',', 'nga', 'naglisod', 'og', 'ginhawa', 'sulod', 'sa', 'iyang', 'lungon', 'samtang', 'gitabang', 'kini', 'sa', 'duha', 'ka', 'lalaki.', 'Human', 'sa', 'maong', 'panghitabo', ',', 'gidala', 'si', 'Montoya', 'sa', 'usa', 'ka', 'ospital', 'sa', 'lungsod', 'sa', 'Babahoyo', 'aron', 'ipadayon', 'ang', 'pagtambal.', 'Nasayop', 'siya', 'pagdeklarar', 'nga', 'patay', 'na', 'niadtong', 'Hunyo', '9', ',', 'apan', 'tinuod', 'na', 'kining', 'namatay', 'tungod', 'sa', 'stroke', 'niadtong', 'Hunyo', '16', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,014
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LATO-LATO', 'NO', 'MORE', '!', 'Nagpahimangno', 'sa', 'publiko', 'ang', 'Food', 'and', 'Drug', 'Administration', '(', 'FDA', ')', 'nga', 'dili', 'mopalit', 'sa', 'dulaan', 'nga', 'lato-lato.', 'Sumala', 'pa', 'sa', 'FDA', ',', 'dili', 'sigurado', 'nga', 'luwas', 'alang', 'sa', 'mga', 'bata', 'ang', 'maong', 'dulaan', 'tungod', 'wala', 'kini', 'moagi', 'sa', 'quality', 'testing.', 'Gisubli', 'sab', 'nila', ',', 'walay', 'certificate', 'of', 'notification', 'ang', 'maong', 'produkto', 'nga', 'usa', 'sa', 'kinahanglanon', 'alang', 'sa', 'pag-apruba', 'sa', 'pagbaligya', 'sa', 'merkado.', 'Gasugod', 'na', 'sab', 'ang', 'maong', 'ahensya', 'sa', 'pagkumpiskar', 'sa', 'naasoy', 'nga', 'dulaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,015
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['637', 'KA', 'EDUCATIONAL', 'SCHOLAR', 'SA', 'NEGOR', ',', 'HATAGAN', 'OG', '₱5,000', 'NGA', 'AYUDA', 'KADA', 'USA', 'Gipatuman', 'ni', 'Gov.', 'Manuel', '"', 'Chaco', '"', 'Sagarbarria', 'ang', 'Educational', 'and', 'Financial', 'Assistance', 'Program', 'sa', '637', 'ka', 'mga', 'scholars', 'sa', 'probinsya', 'sa', 'Negros', 'Oriental.', 'Tumong', 'niini', 'nga', 'mapalig-on', 'ang', 'scholarship', 'program', 'alang', 'sa', 'mga', '"', 'poor', ',', 'underprivileged', 'but', 'deserving', 'students', '"', 'sa', 'probinsya.', 'Makadawat', 'og', 'P5,000', 'kada', 'semeter', 'ang', 'matag', 'estudyante', ',', 'basta', 'ilang', 'imintinar', 'ang', '80', '%', 'nga', 'general', 'passing', 'average.', 'Hingpit', 'nga', 'ipatuman', 'sa', 'gobernador', 'ang', 'P10', 'milyones', 'nga', 'pondo', 'sunod', 'tuig', 'ug', 'pun-on', 'ang', '363', 'na', 'mga', 'bakante', 'sa', 'scholarship', 'program.', 'Dugang', 'pa', ',', 'gitinguha', 'sa', 'gobernador', 'ang', 'pagdungag', 'og', 'pondo', 'alang', 'maong', 'scholarship', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,016
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DOLE-7', ',', 'GIPANGHINGUSGAN', 'ANG', 'PAG-MONITOR', 'SA', 'MGA', 'CHILD', 'LABORER', 'SA', 'NEGOR', 'Gipanghingusgan', 'sa', 'Department', 'of', 'Labor', 'and', 'Employment', 'sa', 'Region', '7', 'ang', 'pag-monitor', 'sa', 'giila', 'nga', 'mga', 'child', 'laborer', 'sa', 'probinsya', 'sa', 'Negros', 'Oriental', 'ug', 'Siquijor.', 'Gitaho', 'sa', 'DOLE-7', 'nga', 'ilang', 'gi-monitor', 'sugod', 'niadtong', 'Mayo', 'ang', '239', 'ka', 'mga', 'child', 'laborer', 'ug', 'ilang', 'mga', 'ginikanan', 'sa', 'munisipalidad', 'sa', 'Basay', 'ug', 'La', 'Libertad', 'ug', 'syudad', 'sa', 'Bayawan', 'ug', 'Guihulngan.', 'Gisubli', 'nila', 'nga', 'kaylap', 'sa', 'naasoy', 'nga', 'mga', 'dapit', 'ang', 'mga', 'child', 'laborer.', 'Sa', '239', 'ka', 'mga', 'child', 'laborers', ',', '104', 'ang', 'gikan', 'sa', 'Bayawan', 'ug', '66', 'sa', 'La', 'Libertad', ',', 'samtang', '49', 'ang', 'natala', 'sa', 'Guihulngan', 'ug', '20', 'sa', 'Basay.', 'Tumong', 'sa', 'departamento', 'nga', 'makatabang', 'pagwagtang', 'sa', 'insidente', 'sa', 'child', 'labor', 'sa', 'tibuok', 'nasud', 'pag-abot', 'sa', '2028', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,017
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', '42', 'anyos', 'nga', 'janitress', 'sa', 'Tuguegarao', 'City', 'ang', 'mapasigarbuhon', 'nga', 'nag-pose', 'human', 'sa', 'iyang', 'klase', 'sa', 'kindergarten.', 'Mogradwar', 'si', 'Remilyn', 'Dimla', 'sa', 'kindergarten', 'karong', 'Hulyo', '14', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,018
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ANGAY', 'NGA', 'POSTURA', 'SA', 'KAMOT', 'ATOL', 'SA', 'PAG-AMPO', 'SA', ''OUR', 'FATHER', ''', 'SA', 'MISA', 'Nagpagawas', 'og', 'usa', 'ka', 'sulat', 'ang', 'Diocese', 'of', 'Dumaguete', 'mahitungod', 'sa', 'angay', 'nga', 'postura', 'sa', 'kamot', 'atol', 'sa', 'pag-ampo', 'sa', '"', 'Our', 'Father', '"', 'sa', 'mga', 'misa.', 'Gisubli', 'sa', 'obispo', 'nga', 'masiguro', 'niini', 'ang', 'klaro', 'ug', 'pagkaparehas', 'nga', 'posisyon', 'sa', 'kamot', 'atol', 'sa', 'misa.', 'Ang', 'Circular', 'Letter', 'No.', '1053-14-2023', ',', 'niepekto', 'niadtong', 'Hunyo', '16', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,019
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PULIS', ',', 'GIPADAKOP', 'ANG', 'ASAWA', 'SAMTANG', 'KAUBAN', 'ANG', 'GIINGONG', 'KABIT', 'NIINI', 'SA', 'BACOLOD', 'CITY', 'Naabtan', 'sa', 'mga', 'awtoridad', 'ang', 'asawa', 'sa', 'usa', 'ka', 'pulis', 'uban', 'sa', 'giingong', 'kabit', 'niini', 'nga', 'anaa', 'sa', 'sulod', 'sa', 'kwarto', 'sa', 'usa', 'ka', 'apartment', 'sa', 'Barangay', 'Singcang-Airport', 'sa', 'Bacolod', 'City.', 'Sumala', 'pa', 'sa', 'report', ',', 'giingong', 'gisulod', 'sa', 'mga', 'awtoridad', 'ang', 'maong', 'apartment', 'sa', 'dihang', 'nakompirmar', 'sa', 'nagreklamong', 'pulis', 'nga', 'anaa', 'sa', 'sulod', 'ang', 'iyang', 'asawa.', 'Sa', 'sulod', ',', 'naabtan', 'ang', 'asawa', 'sa', 'pulis', 'nga', 'nakasuot', 'og', 'sinina', 'nga', 'pangkatulog', 'samtang', 'naghubo', 'ang', 'lalaki', 'nga', 'anaa', 'sa', 'higdaanan.', 'Matud', 'pa', 'sa', 'impormasyon', 'sa', 'kapulisan', ',', 'giingong', 'pila', 'ra', 'kaadlaw', 'nga', 'giabangan', 'sa', 'asawa', 'ug', 'maong', 'lalaki', 'ang', 'naasoy', 'nga', 'apartment.', 'Gisubli', 'sa', 'asawa', 'nga', 'komplikado', 'ang', 'relasyon', 'nila', 'sa', 'iyang', 'bana', 'ug', 'gipasanginlan', 'nga', 'aduna', 'sab', 'laing', 'karelasyon', 'ang', 'iyang', 'bana.', 'Dugang', 'pa', 'sa', 'kapulisan', ',', 'pasakaan', 'gihapon', 'og', 'kasong', 'adultery', 'ang', 'duha', 'bisan', 'paman', 'wala', 'makita', 'sa', 'akto', 'ang', 'pakighilawas', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,020
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', ',', 'NAPUSILAN', 'ANG', 'KAUGALINGON', 'SAMTANG', 'NAGDAMGO', 'NGA', 'GIKAWATAN', 'ANG', 'BALAY', 'Aksidenteng', 'napusilan', 'sa', 'usa', 'ka', 'lalaki', 'sa', 'Estados', 'Unidos', 'ang', 'iyang', 'kaugalingon', 'samtang', 'natulog', 'niadtong', 'Abril', '10', ',', '2023.', 'Gidamgo', 'si', 'Mark', 'Dicara', ',', '62', 'anyos', ',', 'nga', 'aduna'y', 'nisulod', 'sa', 'iyang', 'balay.', 'Tungod', 'niini', ',', 'nikuha', 'siya', 'og', 'pusil', 'ug', 'aksidenteng', 'naunay', 'niini.', 'Napusilan', 'ni', 'Dicara', 'ang', 'iyang', 'batiis', 'ug', 'diha', 'na', 'siya', 'nakamata', 'sa', 'iyang', 'damgo.', 'Dali', 'sab', 'siyang', 'gidala', 'sa', 'usa', 'ka', 'lokal', 'nga', 'ospital', 'aron', 'matambalan.', 'Human', 'sa', 'maong', 'insidente', ',', 'mag-atubang', 'si', 'Dicara', 'og', 'mga', 'kaso', 'bahin', 'sa', 'ilegal', 'nga', 'pagkupot', 'og', 'pusil', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,021
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BROWNOUT', 'NA', 'PUD.', 'Sa', 'ikaduhang', 'higayon', ',', 'walay', 'kuryente', 'na', 'pud', 'sa', 'tibuok', 'Dumaguete', 'City', 'ug', 'sa', 'mga', 'kasikbit', 'nga', 'lungsod', 'niini', 'karong', 'Huwebes', ',', 'June', '15', ',', '2023.', 'Matud', 'pa', 'sa', 'NORECO', 'II', ',', 'kini', 'tungod', 'sa', 'pag-trip', 'off', 'sa', 'linya', 'sa', 'NGCP', 'aron', 'mabalik', 'ang', 'suga', 'sa', 'mga', 'lungsod', 'sa', 'Zamboanguita', 'ug', 'Siaton', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0]
|
cebuaner
|
4,022
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'kuryente', 'gihapon', 'sa', 'pipila', 'ka', 'bahin', 'sa', 'Negros', 'Oriental', 'taliwala', 'sa', 'ulan', 'nga', 'padayong', 'nasinati', 'sa', 'probinsya', 'karong', 'Huwebes', ',', 'June', '15', ',', '2023.', 'Ganinang', 'kaadlawon', ',', 'pakalit', 'nga', 'nag-blackout', 'sa', 'tibuok', 'Dumaguete', 'City', 'ug', 'sa', 'mga', 'kasikbit', 'nga', 'lungsod', 'niini.', 'Sa', 'pagkakaron', ',', 'padayong', 'giayo', 'ang', 'linya', 'sa', 'kuryente', 'sa', 'Dauin', 'ngadto', 'sa', 'Siaton', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0]
|
cebuaner
|
4,023
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakadawat', 'og', 'standing', 'ovation', 'ang', 'Bisaya', 'singer', 'nga', 'si', 'Roland', '"', 'Bunot', '"', 'Abante', 'human', 'kini', 'nitungtong', 'sa', 'entablado', 'sa', 'America', ''s', 'Got', 'Talent.', 'Nakuha', 'sab', 'ni', 'Bunot', 'ang', '"', 'yes', '"', 'votes', 'sa', 'tanang', 'mga', 'hurado', 'sa', 'AGT', 'nga', 'sila', 'si', 'Howie', 'Mandel', ',', 'Simon', 'Cowell', ',', 'Sofia', 'Vergara', ',', 'ug', 'Heidi', 'Klum.', 'Kini', 'human', 'gikanta', 'sa', 'lumad', 'nga', 'taga-Santander', ',', 'Cebu', 'ang', '"', 'When', 'a', 'Man', 'Loves', 'a', 'Woman', '"', 'ni', 'Percy', 'Sledge', 'Martes', 'sa', 'gabii', ',', 'June', '13', ',', '2023.', 'Sa', 'pakighinabi', 'sa', 'mga', 'hurado', 'kaniya', ',', 'gibutyag', 'ni', 'Bunot', 'sa', 'pinulongang', 'Binisaya', 'nga', 'siya', 'usa', 'ka', 'mangingisda', 'ug', 'driver', 'sa', 'habal-habal.', 'Matuman', 'lang', 'kuno', 'niya', 'ang', 'iyang', 'hilig', 'sa', 'pagkanta', 'pinaagi', 'sa', 'mga', 'videokehan', 'sa', 'iyang', 'silingan.', 'Human', 'mapa-wow', 'ang', 'mga', 'hurado', 'ug', 'audience', 'sa', 'AGT', ',', 'gilaomang', 'mopadayon', 'si', 'Bunot', 'sa', 'sunod', 'nga', 'hugna', 'sa', 'maong', 'contest', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,024
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Heads', 'up', ',', 'beshy', '!', 'Walay', 'kuryente', 'karong', 'Dominggo', ',', 'Hunyo', '18', ',', '2023', ',', 'gikan', 'alas-5:30', 'sa', 'buntag', 'hangtud', 'alas-6', 'sa', 'gabii.', 'Usa', 'ka', 'scheduled', 'power', 'service', 'interruption', 'ang', 'ipahigayon', 'sa', 'National', 'Grid', 'Corporation', 'of', 'the', 'Philippines', '(', 'NGCP', ')', 'sa', 'pipila', 'ka', 'dapit', 'sa', 'probinsya.', 'Matud', 'pa', 'sa', 'abiso', 'sa', 'NORECO', 'II', ',', 'wala'y', 'suga', 'gikan', 'sa', 'Calo', ',', 'San', 'Jose', 'ug', 'sa', 'tibuok', 'bahin', 'sa', 'Amlan', ',', 'Tanjay', 'City', 'ug', 'Pamplona.', 'Wala', 'pu'y', 'kuryente', 'sa', 'lugar', 'sa', 'Dobdob', 'ug', 'Calinawan', 'sa', 'Valencia', 'ug', 'Avocado', 'sa', 'Sta.', 'Catalina.', 'Nipasalig', 'sab', 'ang', 'NGCP', 'nga', 'dalion', 'pag-ayo', 'ang', 'mga', 'transmission', 'lines', 'alang', 'sa', 'maong', 'mga', 'dapit.', '|', 'via', 'Reynalyn', 'Labarda', ',', 'correspondent'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,025
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gideklarar', 'sa', 'Malacañang', 'nga', 'regular', 'holiday', 'ang', 'Miyerkules', ',', 'Hunyo', '28', ',', '2023', ',', 'isip', 'pagsaulog', 'sa', 'Eid'l', 'Adha', 'kon', 'Feast', 'of', 'Sacrifice', 'sa', 'atong', 'mga', 'kaigsuonang', 'Muslim', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 7, 0]
|
cebuaner
|
4,026
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'kuryente', 'karong', 'Sabado', ',', 'Hunyo', '17', ',', '2023', ',', 'gikan', 'alas-6', 'sa', 'buntag', 'hangtud', 'alas-6', 'sa', 'gabii.', 'Usa', 'ka', 'scheduled', 'power', 'service', 'interruption', 'ang', 'himuon', 'sa', 'National', 'Grid', 'Corporation', 'of', 'the', 'Philippines', '(', 'NGCP', ')', 'sa', 'pipila', 'ka', 'bahin', 'sa', 'Negros', 'Oriental.', 'Sumala', 'pa', 'sa', 'abiso', 'sa', 'NORECO', 'II', ',', 'wala'y', 'suga', 'ang', 'tibuok', 'bahin', 'sa', 'Santa', 'Catalina', ',', 'Bayawan', 'City', 'ug', 'Basay.', 'Wala', 'pu'y', 'kuryente', 'ang', 'lugar', 'sa', 'Calicanan', 'ug', 'Fatima', 'sa', 'Pamplona', ',', 'ug', 'Sto.', 'Niño', 'sa', 'Tanjay', 'City.', 'Nipasalig', 'sab', 'ang', 'NGCP', 'nga', 'dalion', 'niini', 'pag-ayo', 'ang', 'transmission', 'lines', 'alang', 'sa', 'maong', 'mga', 'dapit.', '|', 'via', 'Reynalyn', 'Labarda', ',', 'correspondent'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]
|
cebuaner
|
4,027
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LA', 'LIBERTAD', ',', 'NAG-APOD-APOD', 'OG', '25KG', 'NGA', 'BUGAS', 'SA', 'MGA', 'RESIDENTE', 'NIINI', 'NGA', 'APEKTADO', 'SA', 'ASF', 'CRISIS', 'Nag-apod-apod', 'ang', 'LGU', 'sa', 'La', 'Libertad', 'og', '25kg', 'nga', 'bugas', 'sa', '11,000', 'ka', 'mga', 'panimalay', 'nga', 'sakop', 'niini.', 'Gihimo', 'kini', 'nga', 'lakang', 'aron', 'mabatukan', 'ang', 'dili', 'maayong', 'epekto', 'sa', 'African', 'Swine', 'Fever', '(', 'ASF', ')', 'sa', 'maong', 'lungsod.', 'Gisugdan', 'niadtong', 'Hunyo', '7', 'ang', 'pagpanghatag', 'og', 'ayudang', 'bugas', 'sa', 'unom', 'ka', 'mga', 'coastal', 'barangay', 'ug', 'mosunod', 'ang', 'nahibiling', '23', 'ka', 'mga', 'upper', 'barangay', 'sa', 'La', 'Libertad.', 'Ang', 'Executive', 'Order', 'No.', '23', 'nga', 'nagdili', 'sa', 'mga', 'buhing', 'baboy', 'ug', 'mga', 'produkto', 'nga', 'may', 'kalabotan', 'sa', 'baboy', 'sa', 'probinsya', ',', 'nakapasamot', 'sa', 'kalisdanan', 'sa', 'panginabuhi.', 'Ang', 'ASF', 'aduna'y', 'dakong', 'epekto', 'sa', 'La', 'Libertad', ',', 'ilabina', 'sa', 'mga', 'residente', 'kinsang', 'panginabuhi', 'nagsalig', 'sa', 'industriya', 'sa', 'baboy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,028
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PARI', ',', 'PATAY', 'HUMAN', 'NIBANGGA', 'ANG', 'GIMANEHONG', 'MOTORSIKLO', 'SA', 'DUMP', 'TRUCK', 'Patay', 'ang', 'usa', 'ka', 'pari', 'human', 'aksidenteng', 'nibangga', 'ang', 'gimanehong', 'motorsiklo', 'niini', 'sa', 'usa', 'ka', 'dump', 'truck', 'sa', 'National', 'Highway', 'sa', 'Barangay', 'Malusay', 'sa', 'Guihulngan', 'City', 'mga', 'alas-4:28', 'sa', 'kadlawon', 'niadtong', 'Hunyo', '13', ',', '2023.', 'Giila', 'ang', 'pari', 'nga', 'si', 'Rev.', 'Father', 'Rexijan', 'Gargoles', 'Jamito', ',', '29', 'anyos', ',', 'ug', 'lumolupyo', 'sa', 'Barangay', 'Dawis', ',', 'Bayawan', 'City.', 'Usa', 'ka', 'pari', 'si', 'Jamito', 'sa', 'Iglesia', 'Filipina', 'Independente', '(', 'IFI', ')', 'nga', 'nakadestino', 'sa', 'Barangay', 'Hilaitan', 'sa', 'Guihulngan', 'City.', 'Matud', 'pa', 'report', 'sa', 'kapulisan', ',', 'nagbiye', 'ang', 'pari', 'sa', 'dihang', 'aksidenteng', 'nibangga', 'ang', 'motorsiklo', 'niini', 'sa', 'nakaparadang', 'dump', 'truck.', 'Gidali', 'pagdala', 'ang', 'pari', 'sa', 'usa', 'ka', 'ospital', 'apan', 'gideklarar', 'kinin', 'dead', 'on', 'arrival', 'sa', 'nag-atiman', 'nga', 'doktor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,029
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BABAYENG', 'GIDEKLARAR', 'NA', 'NGA', 'PATAY', 'OG', '2', 'KA', 'ADLAW', ',', 'NAPALGANG', 'BUHI', 'PA', 'DIAY', 'SULOD', 'SA', 'LUNGON', 'Usa', 'ka', 'tigulang', 'sa', 'Ecuador', 'ang', 'nakamata', 'sulod', 'sa', 'usa', 'ka', 'lungon', 'sa', 'iyang', 'kaugalingong', 'haya.', 'Sa', 'usa', 'ka', 'video', 'nga', 'gi-post', 'sa', 'Twitter', ',', 'nakita', 'si', 'Bella', 'Montoya', ',', '76', 'anyos', ',', 'nga', 'naglugos', 'og', 'ginhawa', 'sulod', 'sa', 'iyang', 'lungon', 'samtang', 'gitabang', 'kini', 'sa', 'duha', 'ka', 'lalaki.', 'Sumala', 'pa', 'sa', 'iyang', 'anak', 'nga', 'si', 'Gilbert', 'Balberán', ',', 'duha', 'ka', 'adlaw', 'nang', 'gideklarar', 'nga', 'patay', 'ang', 'iyang', 'inahan.', 'Dugang', 'pa', 'niya', ',', 'nahibaloan', 'nilang', 'buhi', 'pa', 'ang', 'iyang', 'inahan', 'tungod', 'ginahapak', 'sa', 'walang', 'kamot', 'niini', 'ang', 'iyang', 'lungon', 'human', 'sa', 'lima', 'ka', 'oras', 'nga', 'haya', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,030
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NANGITA', 'OG', 'TRABAHO', '?', 'JOB', 'FAIR', ',', 'IPAHIGAYON', 'SA', 'DUMAGUETE', 'CITY', 'KARONG', 'INDEPENDENCE', 'DAY', 'Aduna'y', 'ipahigayong', 'job', 'fair', 'karong', 'Lunes', ',', 'Hunyo', '12', ',', '2023', ',', 'sa', 'Main', 'Atrium', ',', 'Robinsons', 'Place', 'sa', 'dakbayan', 'sa', 'Dumaguete.', 'Ipahigayon', 'ang', 'Kalayaan', 'Job', 'Fair', 'aron', 'pagsaulog', 'sa', 'ika-125', 'nga', 'anibersaryo', 'sa', 'Philippine', 'Independence', 'and', 'Nationhood', 'uban', 'sa', 'tema', 'nga', '"', 'Kalayaan.', 'Kinabukasan.', 'Kasaysayan', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,031
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAYON', 'VOLCANO', ',', 'GIISA', 'NA', 'SA', 'ALERT', 'LEVEL', '3', 'Giisa', 'na', 'karon', 'ngadto', 'sa', 'Alert', 'Level', '3', 'ang', 'Mayon', 'Volcano', ',', 'sumala', 'pa', 'sa', 'alert', 'level', 'bulletin', 'nga', 'gipagawas', 'sa', 'DOST-PHIVOLCS.', 'Nagpasabot', 'kini', 'nga', 'nagpakita', 'ang', 'Mayon', 'Volcano', 'og', 'dili', 'maayong', 'kalihokan', 'ug', 'gipasidan-an', 'nga', 'adunay', '"', 'increased', 'tendency', 'towards', 'a', 'hazardous', 'eruption.', '"', 'Girekomendar', 'sa', 'Phivolcs', 'ang', 'pagbakwit', 'sa', 'mga', 'tawo', 'nga', 'nagpuyo', 'sulod', 'sa', '6-km', 'radius', 'Permanent', 'Danger', 'Zone', '(', 'PDZ', ')', 'palibot', 'sa', 'maong', 'bulkan.', 'Nihibaloan', 'nga', 'niadtong', 'Lunes', ',', 'Hunyo', '5', ',', '2023', ',', 'giisa', 'sa', 'PHIVOLCS', 'ang', 'alert', 'level', 'status', 'sa', 'Mayon', 'gikan', 'Alert', 'Level', '1', 'ngadto', 'sa', 'Alert', 'Level', '2.', 'Napahimangno', 'sab', 'ang', 'CAAP', 'sa', 'mga', 'flight', 'operators', 'nga', 'likayan', 'ang', 'pagpalupad', 'duol', 'sa', 'bulkan', 'tungod', 'sa', 'posibilidad', 'nga', 'kalit', 'nga', 'pag-ulbo', 'o', 'phreatic', 'eruptions', 'sa', 'maong', 'bulkan', 'nga', 'maghatag', 'og', 'peligro', 'sa', 'mga', 'eroplano', 'na', 'molupad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,032
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makita', 'na', 'karon', 'ang', 'mata', 'sa', 'Severe', 'Tropical', 'Storm', 'nga', '#', 'ChedengPH', '(', '#', 'GUCHOL', ')', 'nga', 'usa', 'ka', 'timailhan', 'nga', 'mokusog', 'kini', 'isip', 'usa', 'ka', 'TYPHOON', 'Category', 'sa', 'mga', 'mosunod', 'nga', 'oras.', 'Sa', 'pagkakaron', ',', 'nagpabiling', 'maba', 'ang', 'tsansa', 'niini', 'nga', 'moigo', 'sa', 'nasud', 'apan', 'gilaomang', 'pakusgon', 'niini', 'ang', 'Southwest', 'Monsoon', 'o', 'Habagat', 'sugod', 'ugmang', 'adlawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,033
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGTIAYON', 'SA', 'MALAYSIA', 'NAGBULAG', 'TUNGOD', 'SA', 'TALAGSA-ONG', 'SAKIT', 'SA', 'DUGO', 'Usa', 'ka', 'magtiayon', 'sa', 'Malaysia', 'ang', 'nagbulag', 'human', 'nahibal-an', 'nga', 'sila', 'parehong', 'adunay', 'thalassemia', ',', 'usa', 'ka', 'sakit', 'sa', 'dugo', 'nga', 'gipasa', 'gikan', 'sa', 'ginikanan', 'ngadto', 'sa', 'anak.', 'Ang', 'maong', 'sakit', 'mahimong', 'moresulta', 'sa', 'anemia', 'ug', 'nagkinahanglan', 'kini', 'og', 'regular', 'nga', 'pag-abono', 'sa', 'dugo', 'kon', 'blood', 'transfusions', 'ug', 'ang', 'bugtong', 'posible', 'nga', 'tambal', 'sa', 'pagkakaron', 'mao', 'ang', 'bone-marrow', 'transplant.', 'Ang', 'magtiayon', 'parehong', 'doktor', 'nga', 'si', 'Farra', 'Diana', 'ug', 'Ashraff.', 'Sa', 'wala', 'pa', 'sila', 'gikasal', ',', 'niining', 'bagohay', 'lang', 'nga', 'mga', 'bulan', 'nila', 'nahibal-an', 'nga', 'pareha', 'silang', 'duha', 'na', 'thalassemia', 'carrier', 'Sakit', 'man', 'para', 'sa', 'duha', 'apan', 'nagdesisyon', 'sila', 'nga', 'magbulag', 'aron', 'malikayan', 'ang', 'mamahimong', 'peligro', 'og', 'mapasa', 'ang', 'maong', 'sakit', 'sa', 'umaabot', 'nilang', 'bata', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,034
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PIPILA', 'KA', 'MGA', 'WALA', 'NAKABAYAD', 'OG', 'UTANG', 'SA', 'ONLINE', 'LENDING', 'APP', ',', 'GIPADALHAN', 'OG', 'BULAK', 'SA', 'PATAY', 'UG', 'LUNGON', 'Pipila', 'ka', 'indibidwal', 'nga', 'na-delay', 'ang', 'pagbayad', 'sa', 'online', 'lending', 'app', 'ang', 'giingong', 'gipadalhan', 'og', 'bulak', 'sa', 'patay', 'ug', 'lungon', 'aron', 'hadlukon.', 'Sumala', 'pa', 'sa', 'report', ',', 'giingong', 'nakahilak', 'na', 'lamang', 'ang', 'usa', 'ka', 'biktima', 'sa', 'dihang', 'nakita', 'niya', 'ang', 'bulak', 'sa', 'patay', 'nga', 'gipadala', 'ngadto', 'niya.', 'Dugang', 'pa', ',', 'giingong', 'tulo', 'lamang', 'kaadlaw', 'nga', 'na-delay', 'ang', 'pagbayad', 'sa', 'utang', 'sa', 'usa', 'ka', 'biktima.', 'Usa', 'sab', 'ka', 'indibidwal', 'ang', 'gipadalhan', 'og', 'lungon', 'tungod', 'sab', 'sa', 'pagkalangan', 'sa', 'pagbayad', 'sa', 'utang.', 'Giawhag', 'sa', 'PNP', 'Anti-Cybercrime', 'Group', 'ang', 'mga', 'biktima', 'sa', 'maong', 'panghitabo', 'nga', 'moduol', 'nila', 'aron', 'makapasaka', 'og', 'kaso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,035
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CANADA', ',', 'NAGTANYAG', 'OG', 'VISA-FREE', 'TRAVEL', 'SA', 'MGA', 'KWALIPIKADONG', 'PILIPINO', 'Mahimo', 'ng', 'mokuha', 'og', 'visa-free', 'air', 'travel', 'ang', 'mga', 'kwalipikadong', 'Pilipino', ',', 'sumala', 'pa', 'sa', 'embahado', 'sa', 'Canada', 'niadtong', 'Miyerkules.', 'Kini', 'human', 'gidungag', 'sa', 'nasud', 'sa', 'North', 'America', 'ang', 'Pilipinas', 'sa', 'Electronic', 'Travel', 'Authorization', '(', 'eTA', ')', 'program', 'niini.', 'Magtugot', 'kini', 'sa', 'mga', 'Pilipino', 'nga', 'aduna'y', 'Canadian', 'visa', 'sulod', 'sa', 'pulo', 'sa', 'tuig', 'ug', 'kadtong', 'naa'y', 'mga', 'non-immigrant', 'visas', 'gikan', 'sa', 'United', 'States', 'nga', 'mo-apply', 'sa', 'eTA', 'kung', 'mobiyahe', 'sa', 'Canada.', 'Aron', 'mahibal-an', 'kung', 'kwalipikado', 'ang', 'usa', 'ka', 'indibidwal', 'ug', 'unsaon', 'pag-apply', ',', 'mahimong', 'bisitahon', 'ang', 'Canadian', 'government', ''s', 'eTA', 'website', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 7, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0]
|
cebuaner
|
4,036
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PHILIPPINE', 'AIRLINES', ',', 'NAGTANYAG', 'OG', 'P125', 'NGA', 'PLITE', 'HANGTOD', 'JUNE', '25', 'Nagtanyag', 'ang', 'Philippine', 'Airlines', 'og', 'P125', 'nga', 'one-way', 'base', 'fare', 'sugod', 'June', '5', 'hangtod', 'June', '25', ',', '2023.', 'Aduna', 'kini', 'domestic', 'travel', 'period', 'gikan', 'Feb.', '1', ',', '2024.', 'Alang', 'sa', 'mga', 'international', 'travelers', ',', 'lakip', 'sa', 'travel', 'period', 'ang', 'South', 'Korea', 'ug', 'Guam', 'gikan', 'July', ';', 'Hong', 'Kong', ',', 'Macau', ',', 'China', ',', 'Taiwan', ',', 'ug', 'Southeast', 'Asia', 'gikan', 'Sept.', '1', ';', 'Japan', 'gikan', 'Jan.', '15', ';', 'North', 'America', ',', 'Middle', 'East', ',', 'ug', 'Australia', 'gikan', 'Feb.', '1.', 'Aron', 'pag-avail', 'sa', 'maong', 'promo', ',', 'mahimong', 'bisitahon', 'ang', 'section', 'on', 'flight', 'ticket', 'deals', 'sa', 'maong', 'airline', 'ug', 'i-click', 'ang', '"', 'book', 'now', '"', 'sa', 'ilalom', 'sa', 'napiling', 'destinasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 0, 5, 6, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,037
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CAAP', ',', 'GIDID-AN', 'ANG', 'MGA', 'EROPLANO', 'PAGLUPAD', 'DUOL', 'SA', 'KANLAON', 'VOLCANO', 'TUNGOD', 'SA', 'ABNORMAL', 'NGA', 'KAHIMTANG', 'NIINI', 'Gidili', 'sa', 'Civil', 'Aviation', 'Authority', 'of', 'the', 'Philippines', '(', 'CAAP', ')', 'ang', 'mga', 'eroplano', 'sa', 'paglupad', 'duol', 'sa', 'Taal', ',', 'Mayon', ',', 'ug', 'Kanlaon', 'volcanoes.', 'Ang', 'Kanlaon', 'Volcano', 'anaa', 'sa', 'alert', 'level', '1', '(', 'Abnormal', 'Condition', ')', 'kon', 'nagpakita', 'sa', 'abnormal', 'nga', 'kahimtang.', 'Gitambagan', 'ang', 'mga', 'flight', 'operators', 'nga', 'likayan', 'ang', 'pagpalupad', 'duol', 'sa', 'bulkan', 'tungod', 'sa', 'posibilidad', 'nga', 'kalit', 'nga', 'pag-ulbo', 'o', 'phreatic', 'eruptions', 'sa', 'maong', 'bulkan', 'nga', 'maghatag', 'og', 'peligro', 'sa', 'mga', 'eroplano', 'na', 'molupad.', 'Dugang', 'pa', ',', 'gidili', 'sab', 'ang', 'pagsulod', 'sa', '4km', 'radius', 'sa', 'permanent', 'danger', 'zone', 'sa', 'Kanlaon', 'Volcano', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0]
|
cebuaner
|
4,038
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahimo', 'nang', 'usa', 'ka', 'tropical', 'depression', 'ang', 'low', 'pressure', 'area', '(', 'LPA', ')', 'nga', 'gibantayan', 'sa', 'silangang', 'bahin', 'sa', '#', 'Visayas', ',', 'sumala', 'pa', 'sa', 'PAGASA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,039
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PLDT', 'NITAHO', 'NGA', 'DUNAY', 'PROBLEMA', 'SA', 'INTERNET', 'NIINI', 'Gikumpirmar', 'sa', 'PLDT', 'nga', 'nakasinati', 'ang', 'mga', 'subscriber', 'niini', 'og', 'hinay', 'nga', 'internet', 'connection', 'tungod', 'kay', 'nawad-an', 'og', 'bandwidth', 'capacity', 'and', 'mga', 'submarine', 'cable', 'partners', 'niini.', '"', 'We', 'are', 'now', 'working', 'with', 'our', 'partners', 'to', 'provide', 'alternate', 'capacity', 'that', 'would', 'restore', 'the', 'browsing', 'experience', 'in', 'the', 'next', 'few', 'hours', ',', '"', 'matod', 'pa', 'sa', 'PLDT.', 'Beshie', ',', 'nagkaproblema', 'ba', 'pud', 'ka', 'sa', 'imong', 'internet', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,040
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nisulod', 'na', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'ang', 'gibantayang', 'aktibo', 'nga', 'LPA', 'sa', 'silangang', 'bahin', 'sa', '#', 'Visayas.', 'Aduna', 'kini', 'taas', 'nga', 'posibilidad', 'nga', 'mahimong', 'bagyo', 'sa', 'mga', 'mosunod', 'nga', 'oras.', 'Kung', 'mahimo', 'na', 'kining', 'bagyo', 'samtang', 'anaa', 'sa', 'sulod', 'sa', 'PAR', ',', 'tawgon', 'kining', '#', 'Chedeng', 'sa', 'PAGASA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 7, 0, 3, 0]
|
cebuaner
|
4,041
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INDONESIAN', 'BILLIONAIRE', 'MINGDONAR', 'OG', 'P41-M', 'SA', 'PILIPINAS', 'Nagdonar', 'ang', 'usa', 'ka', 'Indonesian', 'businessman', 'og', 'kapin', 'P41', 'milyones', 'ngadto', 'sa', 'gobyerno', 'sa', 'Pilipinas', 'aron', 'suportahan', 'ang', 'mga', 'social', 'programs', 'ni', 'President', 'Ferdinand', 'Marcos', 'Jr.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Malacañang', 'niadtong', 'Huwebes', ',', 'Hunyo', '1', ',', '2023.', 'Si', 'Tahir', 'ang', 'nagtukod', 'sa', 'Mayapada', 'Group', 'diin', 'aduna', 'sab', 'siya'y', 'negosyo', 'sa', 'financial', ',', 'healthcare', ',', 'hotels', ',', 'media', ',', 'ug', 'mining', 'sectors.', 'Interesado', 'sab', 'si', 'Tahir', 'nga', 'mo-invest', 'sa', 'Pilipinas', 'sama', 'nalang', 'sa', 'pagtukod', 'og', 'mga', 'hospitals.', 'Nahibaloan', 'nga', 'si', 'Tahir', 'nakaila', 'sa', 'mga', 'Marcoses', 'human', 'sila', 'nahimamat', 'sa', 'dihang', 'naka-exile', 'kini', 'sa', 'Hawaii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,042
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAGASA', ',', 'GIBANTAYAN', 'ANG', '2', 'KA', 'LPA', 'NGA', 'ANAA', 'SA', 'GAWAS', 'SA', 'PAR', 'Gibantayan', 'karon', 'sa', 'PAGASA', 'ang', 'duha', 'ka', 'low', 'pressure', 'area', '(', 'LPA', ')', 'nga', 'anaa', 'pa', 'sa', 'gawas', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', '.', 'Ang', 'duha', 'ka', 'LPA', 'mahimong', 'makapausbaw', 'sa', 'southeast', 'monsoon', 'o', 'habagat', 'sa', 'nasud.', 'Sumala', 'pa', 'sa', 'PAGASA', ',', 'nahimutang', 'ang', 'unang', 'LPA', 'sa', 'silangang', 'bahin', 'sa', 'Visayas.', 'Posible', 'nga', 'mahimo', 'kining', 'usa', 'ka', 'tropical', 'cyclone', 'sa', 'tunga-tunga', 'ning', 'semana', 'apan', 'gilaomang', 'dili', 'kini', 'mag-landfall.', 'Samtang', 'ang', 'ikaduhang', 'LPA', 'anaa', 'sa', 'West', 'Philippine', 'Sea', 'ug', 'mopaingon', 'sa', 'Southern', 'China', 'sa', 'tunga-tunga', 'ning', 'semana.', 'Dugang', 'pa', 'sa', 'state', 'weather', 'bureau', ',', 'mahimo', 'sab', 'nga', 'maporma', 'sa', 'West', 'Philippine', 'Sea', 'ang', 'ikatulong', 'LPA', 'gikan', 'sa', 'mga', 'salin', 'sa', 'ikaduhang', 'LPA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,043
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'eksena', 'sa', 'pag-abot', 'sa', 'patayng', 'lawas', 'ni', 'kanhi', 'Gob.', 'Guido', 'Reyes', 'sa', 'Negros', 'Oriental', 'karong', 'adlawa', ',', 'Hunyo', '3', ',', '2023', ',', 'human', 'siya', 'nitaliwan', 'niadtong', 'Miyerkules', ',', 'Mayo', '31.', 'Gihaya', 'si', 'Reyes', 'sa', 'Kapitolyo', 'Sabado', 'sa', 'buntag', 'una', 'kini', 'gidala', 'sa', 'iyang', 'lungsod', 'nga', 'natawhan', 'sa', 'Guihulngan', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0]
|
cebuaner
|
4,044
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Anaa', 'na', 'karon', 'sa', 'half-mast', 'ang', 'bandila', 'sa', 'Pilipinas', 'sa', 'Freedom', 'Park', 'atbang', 'sa', 'Negros', 'Oriental', 'Provincial', 'Capitol', 'isip', 'pahasubo', 'sa', 'pagpanaw', 'ni', 'Governor', 'Guido', 'Reyes', 'karong', 'adlawa', ',', 'Mayo', '31', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,045
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'nay', 'bag-ong', 'gobernador', 'ang', 'Negros', 'Oriental.', 'Kini', 'human', 'nanumpa', 'si', 'Vice', 'Governor', 'Manuel', '"', 'Chaco', '"', 'Sagarbarria', 'isip', 'bag-ong', 'lider', 'sa', 'probinsya', 'human', 'nipanaw', 'karong', 'adlawa', 'si', 'Governor', 'Guido', 'Reyes.', 'Si', 'Sagarbarria', 'gikan', 'sa', 'banay', 'sa', 'mga', 'politiko.', 'Ang', 'iyang', 'amahan', 'nga', 'si', 'Chiquiting', 'maoy', 'representante', 'sa', 'ikaduhang', 'distrito', 'sa', 'Negros', 'Oriental', ',', 'samtang', 'ang', 'iyang', 'inahan', 'nga', 'si', 'Maisa', 'maoy', 'bise', 'mayor', 'sa', 'dakbayan', 'sa', 'Dumaguete', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,046
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nipanaw', 'na', 'si', 'Governor', 'Jorge', 'Carlo', 'Joan', '"', 'Guido', '"', 'Reyes', 'karong', 'adlawa', ',', 'Mayo', '31', ',', '2023.', 'Kini', 'gikumpirmar', 'ni', 'Provincial', 'Administrator', 'Karen', 'Molas', 'ngadto', 'sa', 'Yes', 'The', 'Best', 'Dumaguete', 'sa', 'usa', 'ka', 'pahinabi', 'sa', 'telepono.', 'Namatay', 'si', 'Reyes', 'kapin', '2', 'ka', 'bulan', 'sukad', 'niya', 'gipulihan', 'si', 'Gov.', 'Roel', 'Degamo', 'kinsa', 'gipatay', 'niadtong', 'Marso', '4.', 'Sumala', 'pa', 'ni', 'Molas', ',', 'namatay', 'si', 'Reyes', 'human', 'ang', 'taas', 'nga', 'panahon', 'nga', 'nakigbisog', 'kini', 'sa', 'sakit', ',', 'apan', 'wala', 'kini', 'naghatag', 'og', 'dugang', 'detalye.', 'Mao', 'kini', 'ang', 'unang', 'higayon', 'human', 'ang', 'kapin', 'usa', 'ka', 'dekada', 'nga', 'nagsagunson', 'ang', 'pagkamatay', 'sa', 'duha', 'ka', 'gobernador', 'sa', 'probinsya.', 'Niadtong', '2010', ',', 'namatay', 'si', 'Gov.', 'Emilio', '"', 'Dodo', '"', 'Macias', 'II', 'pipila', 'lamang', 'ka', 'semana', 'human', 'siya', 'nidaog', 'sa', 'piniliay', 'niadtong', 'tuiga.', 'Ang', 'nipuli', 'niya', 'nga', 'si', 'Gov.', 'Agustin', 'Perdices', ',', 'namatay', 'sab', 'sa', 'sakit', 'niadtong', 'Enero', '2011', ',', 'pipila', 'ka', 'bulan', 'lang', 'human', 'niya', 'gipulihan', 'si', 'Macias', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
|
cebuaner
|
4,047
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE-CVIRAA', 'MEDALISTS', ',', 'MAKADAWAT', 'OG', 'CASH', 'INCENTIVES', 'GIKAN', 'SA', 'SYUDAD', 'Nihatag', 'ang', 'City', 'Government', 'sa', 'Dumaguete', 'City', 'og', 'cash', 'incentives', 'sa', 'tanang', 'medalists', 'sa', 'bag-ohay', 'lang', 'nahuman', 'nga', 'Central', 'Visayas', 'Athletics', 'Associations', 'Meet', '(', 'CVIRAA', ')', 'nga', 'gipahigayon', 'sa', 'syudad', 'sa', 'Carcar', ',', 'Cebu.', 'Makadawat', 'og', 'P2,500', 'ang', 'mga', 'gold', 'medalists', ',', 'P2,000', 'sa', 'mga', 'silver', 'medalist', ',', 'ug', 'P1,500', 'sa', 'mga', 'bronze', 'medalist.', 'Makadawat', 'sab', 'ang', 'mga', 'coach', 'sa', 'mga', 'nidaog', 'og', 'cash', 'incentives', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,048
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Taas-taas', 'ang', 'atong', 'weekend', 'sa', 'ikaduhang', 'semana', 'sunod', 'bulan', 'tungod', 'aduna'y', 'usa', 'ka', 'regular', 'holiday', 'sa', 'June', '12', ',', 'isip', 'pagsaulog', 'sa', 'Independence', 'Day', 'sa', 'Pilipinas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0]
|
cebuaner
|
4,049
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BASKETBOL', ',', 'NISANGKO', 'SA', 'RAMBOL', 'Usa', 'ka', 'kagubot', 'ang', 'nahitabo', 'sa', 'gipahigayong', 'basketball', 'game', 'sa', 'Tanjay', 'City', 'niadtong', 'Lunes', ',', 'May', '29', ',', '2023.', 'Sa', 'video', 'nga', 'gi-post', 'sa', 'netizen', 'nga', 'si', 'Von', 'Anjielo', 'Ferolino', ',', 'makita', 'nga', 'nagsumbagay', 'ang', 'referee', 'ug', 'usa', 'sa', 'mga', 'giingong', 'audience.', 'Makita', 'sab', 'ang', 'pipila', 'ka', 'mga', 'indibidwal', 'nga', 'nitabang', 'sa', 'pagbulag', 'sa', 'mga', 'nagsumbagay.', 'Sumala', 'pa', 'sa', 'taho', 'sa', 'kapulisan', ',', 'giingong', 'nagsugod', 'sa', 'trashtalk', 'ang', 'maong', 'sumbagay', 'human', 'gibugalbugalan', 'sa', 'usa', 'ka', 'audience', 'member', 'ang', 'referee', 'sa', 'dula', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,050
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'lugar', 'sa', 'Negros', 'Oriental', 'ang', 'nagsuspenso', 'sa', 'klase', 'karong', 'Martes', ',', 'Mayo', '30', ',', '2023', ',', 'tungod', 'sa', 'pag-ulan', 'nga', 'dala', 'sa', 'habagat', 'nga', 'gipakusgan', 'sa', 'Bagyong', '#', 'BettyPH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,051
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gatusan', 'ka', 'aplikante', 'nga', 'ganahang', 'moskuwela', 'sa', 'Negros', 'Oriental', 'State', 'University', '(', 'NORSU', ')', 'ang', 'padayong', 'nagtalay', 'karon', 'gawas', 'sa', 'campus', 'niini', 'karong', 'alas-9', 'sa', 'buntag', ',', 'Mayo', '30', ',', '2023.', 'Kini', 'aron', 'pagkuha', 'sa', 'NORSU', 'Online', 'Admission', 'Test', '(', 'NOAT', ')', 'nga', 'nagsugod', 'niadtong', 'Lunes', ',', 'Mayo', '29', ',', 'ug', 'molungtad', 'hangtud', 'Hunyo', '30', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,052
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GATOSAN', 'KA', 'APLIKANTE', 'SA', 'NORSU', ',', 'NAGTALAY', 'GIHAPON', 'BISA’G', 'GABIING', 'DAKO', 'Tungang', 'gabii', 'na', 'apan', 'gatosan', 'gihapon', 'ka', 'estudyante', 'nga', 'ganahang', 'moskuwela', 'sa', 'Negros', 'Oriental', 'State', 'University', 'ang', 'naglinya', 'aron', 'pagkuha', 'sa', 'NORSU', 'Online', 'Admission', 'Test', '(', 'NOAT', ')', '.', 'Gikan', 'sa', 'Gate', '3', 'sa', 'NORSU', 'Dumaguete', 'Campus', ',', 'niabot', 'na', 'ang', 'linya', 'sa', 'mga', 'aplikante', 'hangtud', 'sa', 'Dumaguete', 'Perpetual', 'Church.', 'Una', 'nang', 'gipasidaan', 'sa', 'tunghaan', 'ang', 'mga', 'aplikante', 'nga', 'layo', 'sa', 'Dumaguete', 'nga', 'kuhaon', 'ang', 'NOAT', 'online', 'gikan', 'sa', 'ilang', 'mga', 'balay.', 'Apan', 'duna', 'gihapoy', 'mga', 'estudyante', 'nga', 'nisugal', 'pagbiyahe', 'nganhi', 'sa', 'dakbayan', 'aron', 'madawat', 'unta', 'sa', 'NORSU', 'alang', 'sa', 'kolehiyo.', 'Ipahigayon', 'ang', 'NOAT', 'gikan', 'Mayo', '29', 'hangtud', 'Hunyo', '30', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,053
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', ',', 'GIPANGHINGUSGAN', 'ANG', 'PAGBAKUNA', 'SA', 'MGA', 'BATA', 'BATOK', 'MEASLES', 'UG', 'RUBELLA', 'Gipanghingusgan', 'pa', 'sa', 'City', 'Governement', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'ang', 'pagbakuna', 'sa', 'mga', 'kwalipikadong', 'bata', 'nga', 'nag-edad', 'og', '9', 'months', 'hangtod', '5', 'years', 'old', 'batok', 'sa', 'measles', 'ug', 'rebella.', 'Gipatuman', 'karon', 'ang', 'supplemental', 'immunization', 'activity', 'City', 'Health', 'Office', ',', 'sa', 'DOH', 'sa', 'Negros', 'Oriental', 'ug', 'Provincial', 'Health', 'Office.', 'Tumong', 'sa', 'maong', 'aktibidad', 'ang', 'pagbakuna', 'sa', 'mga', 'kwalipikadong', 'bata', 'batok', 'measles', 'ug', 'rubella', 'aron', 'modako', 'sila', 'nga', 'biskog', 'ang', 'panglawas', 'ug', 'malikayan', 'ang', 'mga', 'serious', 'disabilities.', 'Alang', 'sa', 'schedules', ',', 'mahimong', 'makig-coordinate', 'ang', 'mga', 'ginikanan', 'sa', 'ilang', 'tagsa-tagsa', 'nga', 'mga', 'barangay', 'health', 'centers', 'o', 'maghulat', 'sa', 'mga', 'health', 'workers', 'sa', 'house-to-house', 'immunization', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 3, 0, 5, 6, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,054
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mas', 'nikusog', 'pa', 'ang', 'Super', 'Typhoon', '#', 'Mawar', 'samtang', 'gipuntirya', 'niini', 'ang', 'kadagatan', 'sa', 'Pilipinas.', 'Dala', 'karon', 'sa', 'maong', 'bagyo', 'ang', 'kakusgon', 'sa', 'hangin', 'nga', 'moabot', 'sa', '205', 'kph', 'ug', 'paghuros', 'nga', 'moabot', 'sa', '290', 'kph.', 'Naglihok', 'kini', 'paingon', 'sa', 'amihanan-kasadpang', 'direksyon', 'nga', 'aduna'y', 'kapaspason', 'nga', '15', 'kph.', 'Gilaomang', 'mokusog', 'pa', 'kini', 'samtang', 'pasulod', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'tungod', 'sa', '"', 'very', 'favorable', '"', 'nga', 'kadagatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,055
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', ',', 'NAGTANYAG', 'OG', 'P88', 'NGA', 'PLITE', 'HANGTOD', 'MAY', '27', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'P88', 'nga', 'one-way', 'base', 'fare', 'alang', 'sa', 'ilang', 'bag-ong', 'seat', 'sale', 'paingon', 'sa', 'local', 'ug', 'international', 'destinations.', 'Lakip', 'sa', 'international', 'destinations', 'ang', 'Taipei', ',', 'Tokyo', ',', 'Macau', ',', 'ug', 'Ho', 'Chi', 'Minh', ',', 'samtang', 'apil', 'sa', 'local', 'destinations', 'ang', 'Laoag', ',', 'Puerto', 'Princesa', ',', 'Bacolod', ',', 'ug', 'Davao.', 'Hangtod', 'lamang', 'sa', 'May', '27', 'ang', 'P88', 'nga', 'promo', 'ug', 'aduna'y', 'travel', 'period', 'gikan', 'June', '1', 'hangtod', 'Nov.', '30', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,056
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makita', 'na', 'sab', 'ang', 'mata', 'sa', 'Super', 'Typhoon', '#', 'Mawar', 'human', 'niini', 'gikusokuso', 'ang', '#', 'Guam', 'kagahapong', 'adlawa.', 'Gikabalak-ang', 'mosulod', 'kini', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'ugma', 'sa', 'gabii', 'o', 'sa', 'kadlawon', 'sa', 'Sabado.', 'Sa', 'oras', 'nga', 'mosulod', 'kini', 'sa', 'PAR', ',', 'tawgon', 'kini', 'sa', 'PAGASA', 'og', '#', 'BettyPH.', 'Sa', 'pagkakaron', ',', 'nagpabiling', 'maba', 'ang', 'posibilidad', 'nga', 'mo-landfall', 'kini', 'sa', 'nasud.', 'Apan', ',', 'gilaoman', 'karon', 'nga', 'mas', 'magkaduol', 'kini', 'sa', '#', 'ExtremeNorthernLuzon', 'sa', 'Lunes-Miyerkules', '(', 'May', '29-31', ')', ',', 'isip', 'usa', 'ka', 'makadaot', 'nga', 'bagyo.', 'Sumala', 'pa', 'sa', 'Japan', 'Meteorological', 'Agency', '(', 'JMA', ')', ',', 'posibleng', 'moabot', 'ang', 'bagyo', 'sa', '"', 'VIOLENT', '"', 'nga', 'kategorya', 'pagsulod', 'niini', 'sa', 'PAR', 'nga', 'adunay', 'kakusgon', 'nga', 'moabot', 'sa', '205', 'kph', 'ug', 'paghuros', 'nga', '290', 'kph.', 'Kinahanglang', 'kining', 'pangandaman', 'ilabi', 'na', 'sa', 'mga', 'nagpuyo', 'sa', 'Hilagang', 'Luzon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0]
|
cebuaner
|
4,057
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TEENAGER', 'SA', 'TAIWAN', ',', 'NAPALGANG', 'PATAY', 'HUMAN', 'NAGPAKASAL', 'SA', 'LALAKI', 'NGA', 'BAG-O', 'RA', 'NIYANG', 'NAKAILA', 'Usa', 'ka', '18-anyos', 'nga', 'high', 'school', 'student', 'sa', 'Taiwan', 'ang', 'napalgang', 'patay', 'duha', 'lamang', 'ka', 'oras', 'human', 'giingong', 'naminyo', 'sa', 'usa', 'ka', 'lalaki', 'nga', 'kaduha', 'pa', 'lang', 'niya', 'nahimamat', ',', 'sumala', 'pa', 'sa', 'inahan', 'sa', 'biktima.', 'Giila', 'ang', 'estudyante', 'sa', 'pangalang', 'Lai', ',', 'usa', 'ka', 'lalaki', ',', '18', 'anyos', ',', 'ug', 'residente', 'sa', 'Taichung', 'sa', 'naasoy', 'nga', 'nasud.', 'Si', 'Lain', 'unta', 'ang', 'mo-inherit', 'kon', 'makapanunod', 'sa', 'mga', 'kabtangan', 'nga', 'nagkantidad', 'og', '$', '500m', 'gikan', 'sa', 'iyang', 'amahan.', 'Nadiskobrehan', 'ang', 'iyang', 'patayng', 'lawas', 'niadtong', 'May', '4', 'nga', 'nagbuy-od', 'sa', 'yuta', 'sa', 'gawas', 'sa', 'usa', 'ka', 'apartment', 'building', 'sa', 'Beitun', 'district.', 'Giila', 'sab', 'ang', 'iyang', 'bana', 'og', 'duha', 'ka', 'oras', 'nga', 'si', 'Hsia', ',', 'usa', 'ka', 'lalaki', ',', '26', 'anyos', ',', 'kinsa', 'nagpuyo', 'sa', 'ika-10th', 'floor', 'sa', 'maong', 'apartment', 'sa', 'nahisgutang', 'lugar.', 'Sa', 'usa', 'ka', 'press', 'conference', 'nga', 'gipahigayon', 'sa', 'inahan', 'ni', 'Lai', ',', 'nga', 'si', 'Chen', ',', 'gipahibalo', 'niya', 'nga', 'nag-alegar', 'sila', 'og', 'foul', 'play', 'sa', 'kamatayon', 'sa', 'iyang', 'anak.', 'Namatay', 'si', 'Lai', 'usa', 'lang', 'ka', 'adlaw', 'pagkahuman', 'sa', 'pagsunog', 'sa', 'iyang', 'amahan', ',', 'nga', 'namatay', 'niadtong', 'katapusang', 'semana', 'sa', 'Abril.', 'Matod', 'pa', 'sa', 'iyang', 'mga', 'abogado', 'nga', 'ang', 'pamilya', 'nagtuo', 'nga', 'si', 'Lai', ',', 'nga', 'nakapanunod', 'sa', 'mga', '30', 'ka', 'mga', 'properties', 'ug', 'kwarta', 'maoy', 'hinungdan', 'sa', 'pagpatay.', 'Dugang', 'pa', ',', 'si', 'Hsia', 'nga', 'primary', 'suspect', 'sa', 'pagpatay', 'ug', 'gibuhian', 'kini', 'sa', 'pyansa', 'nga', 'nagkantidad', 'og', '$', '300,000', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,058
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAGASA', ',', 'GIBANTAYAN', 'ANG', 'POSIBLENG', 'MAHIMONG', ''SUPER', 'TYPHOON', ''', 'GAWAS', 'SA', 'PAR', 'Gibantayan', 'karon', 'sa', 'PAGASA', 'ang', 'usa', 'ka', 'tropical', 'cyclone', 'nga', 'aduna'y', 'posibilidad', 'nga', 'mahimong', 'super', 'typhoon', 'gawas', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', '.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'maong', 'state', 'weather', 'bureau', 'karong', 'adlawa', ',', 'May', '22', ',', '2023.', 'Sa', 'ilang', 'pinakabag-ong', 'public', 'weather', 'forecast', ',', 'ulahing', 'nakita', 'ang', 'bagyo', 'mga', '2,330', 'km', 'sa', 'silangang', 'bahin', 'sa', 'Mindanao.', '"', 'It', 'has', 'maximum', 'sustained', 'winds', 'of', '130', 'kph', 'near', 'the', 'center', 'and', 'gusts', 'of', 'up', 'to', '160', 'kph.', 'It', 'is', 'moving', 'west', 'northweatward', 'at', '15', 'kph', ',', '"', 'matud', 'pa', 'sa', 'report.', 'Apan', 'gisubli', 'ni', 'weather', 'forecaster', 'Anna', 'Clauren-Jorda', 'nga', 'aduna'y', 'low', 'probability', 'ang', 'maong', 'bagyo', 'nga', 'mag-landfall', 'sa', 'Pilipinas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,059
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PATAYNG', 'LAWAS', ',', 'NAPALGAN', 'SA', 'BAYBAYON', 'SA', 'STA.', 'CATALINA', 'Usa', 'ka', 'patayng', 'lawas', 'sa', 'tigulang', 'ang', 'nakit-an', 'sa', 'baybayon', 'sa', 'Sitio', 'Talisay', ',', 'Barangay', 'Poblacion', 'sa', 'Sta.', 'Catalina', 'mga', 'alas-7:10', 'sa', 'buntag', 'niadtong', 'May', '20', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'sa', 'Sta.', 'Catalina', 'ang', 'biktima', 'nga', 'si', 'Veronica', 'Iligan', 'Padilla', ',', '67', 'anyos', ',', 'minyo', ',', 'ug', 'lumolupyo', 'sa', 'Purok', '3', ',', 'Barangay', 'Caranoche', 'sa', 'naasoy', 'nga', 'lungsod.', 'Matud', 'pa', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'nitambong', 'si', 'Padilla', 'sa', 'birthday', 'celebration', 'sa', 'iyang', 'pag-umangkon', 'ug', 'nag-inom-inom', 'sa', 'dagat', 'sa', 'Villareal', 'sa', 'Bayawan', 'City.', 'Ulahing', 'nakit-an', 'ang', 'biktima', 'mga', 'alas-3:00', 'sa', 'hapon', 'niadtong', 'May', '19', ',', '2023.', 'Sumala', 'pa', 'sa', 'mga', 'kabanay', 'sa', 'biktima', 'nga', 'aduna', 'kini', 'kanhing', 'record', 'sa', 'Alzheimer's.', 'Dugang', 'pa', 'sa', 'kapulisan', ',', 'wala'y', 'timailhan', 'sa', 'physical', 'injury', 'ang', 'nakita', 'sa', 'lawas', 'sa', 'biktima.', 'Gitambagan', 'sab', 'sa', 'kapulisan', 'ang', 'pamilya', 'sa', 'biktima', 'nga', 'ipaubos', 'kini', 'sa', 'autopsy', 'aron', 'masuta', 'ang', 'hinungdan', 'sa', 'kamatayon', 'niini', ',', 'apan', 'gibalibaran', 'kini', 'sa', 'pamilya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,060
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'KASO', 'SA', 'ASF', 'NAKOMPIRMAR', 'SA', 'DAUIN', ',', 'PIPILA', 'KA', 'BABOY', 'SA', 'LUNGSOD', 'GIPAMATAY', 'Nagpositibo', 'sa', 'African', 'Swine', 'Fever', '(', 'ASF', ')', 'ang', 'pipila', 'ka', 'mga', 'baboy', 'nga', 'namatay', 'sa', 'Purok', '1', 'ug', '2', 'sa', 'Barangay', 'Maayongtubig', 'sa', 'lungsod', 'sa', 'Dauin.', 'Mao', 'kini', 'ang', 'gikompirmar', 'ni', 'Mayor', 'Galicano', 'Truita', 'pinaagi', 'sa', 'Executive', 'Order', 'No.', '2023-19.1.', 'Base', 'sa', 'laboratory', 'samples', 'nga', 'gipadala', 'ngadto', 'sa', 'Bureau', 'of', 'Animal', 'Industry-Central', 'Office', ',', 'kompirmado', 'nga', 'nagpositibo', 'sa', 'ASF', 'virus', 'ang', 'mga', 'namatay', 'nga', 'baboy', 'sa', 'naasoy', 'nga', 'lugar.', 'Ubos', 'sa', 'EO', '2023-19.1', ',', 'temporaryo', 'nga', 'ginadili', 'ang', 'pagpasulod', 'ug', 'pagpagawas', 'sa', 'buhi', 'nga', 'mga', 'baboy', 'nga', 'naglakip', 'sa', 'fresh', 'and', 'frozen', 'products.', 'Ginadili', 'sab', 'ang', 'pag-ihaw', 'sa', 'mga', 'baboy', 'ug', 'kinahanglang', 'patyon', 'ang', 'tanang', 'baboy', 'nga', 'anaa', 'sa', '500-meter', 'radius', 'gikan', 'sa', 'maong', 'dapit.', 'Gimandoan', 'sa', 'LGU', 'Dauin', 'uban', 'sa', 'koordinasyon', 'sa', 'Provincial', 'Veterinary', 'Office', 'nga', 'ipatuman', 'dayon', 'ang', 'maong', 'kamanduan', 'aron', 'malikayan', 'ang', 'pagkuyanap', 'sa', 'ASF', 'virus', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 7, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0]
|
cebuaner
|
4,061
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TRAHEDYA', 'SA', 'PALITON', 'BEACH', ':', 'BATA', 'PATAY', ',', 'BABAYE', 'ANGOL', 'SA', 'DISGRASYA', 'SA', 'BANGKA', 'SA', 'SIQUIJOR', 'Patay', 'ang', 'usa', 'ka', '6', 'anyos', 'nga', 'batang', 'babaye', 'atol', 'sa', 'karera', 'sa', 'mga', 'bangka', 'nga', 'natapos', 'sa', 'disgrasya', 'sa', 'inilang', 'Paliton', 'Beach', 'sa', 'lungsod', 'sa', 'San', 'Juan', ',', 'Siquijor', 'kagahapon', 'sa', 'buntag', ',', 'Mayo', '21', ',', '2023.', 'Usa', 'pud', 'ka', '24-anyos', 'nga', 'babaye', 'ang', 'naangol', 'sa', 'maong', 'trahedya', 'human', 'kini', 'makahiagom', 'og', 'samad', 'sa', 'ulo.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'naglumba', 'ang', 'pipila', 'ka', 'bangka', 'atol', 'sa', 'usa', 'ka', '"', 'Bangkarera', '"', 'kagahapon', 'sa', 'naasoy', 'nga', 'baybayon.', 'Giklaro', 'sa', 'San', 'Juan', 'PNP', 'nga', 'dunay', 'permit', 'ang', 'maong', 'lumba.', 'Atol', 'sa', 'maong', 'lumba', ',', 'nawad-an', 'og', 'kontrol', 'ang', 'usa', 'sa', 'mga', 'nisalmot', 'nga', 'bangka', 'ug', 'nilahos', 'kini', 'pabalik', 'sa', 'baybayon.', 'Tungod', 'niini', ',', 'naligsan', 'ang', 'usa', 'ka', 'batang', 'babaye', 'nga', 'maoy', 'hinungdan', 'sa', 'kamatayon', 'niini.', 'Anaa', 'na', 'karon', 'sa', 'kustodiya', 'sa', 'kapulisan', 'sa', 'San', 'Juan', 'ang', 'driver', 'sa', 'nakaligis', 'nga', 'bangka.', 'Gikatahong', 'pasakaan', 'kini', 'og', 'mga', 'tukmang', 'kaso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,062
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['4', 'KA', 'GIINGONG', 'NPA', 'PATAY', 'SA', 'PINUSILAY', 'SA', 'GUIHULNGAN', 'CITY', 'Upat', 'ka', 'giingong', 'sakop', 'sa', 'rebeldeng', 'New', 'People’s', 'Army', '(', 'NPA', ')', 'ang', 'napatay', 'atol', 'sa', 'pinusilay', 'sa', 'maong', 'grupo', 'ug', 'sa', 'militar', 'sa', 'bukirang', 'bahin', 'sa', 'Guihulngan', 'City', 'ganinang', 'buntag', ',', 'Mayo', '21', ',', '2023.', 'Kini', 'sumala', 'pa', 'sa', 'pamahayag', 'sa', 'Philippine', 'Army.', 'Gibutyag', 'sab', 'sa', 'militar', 'nga', 'dunay', 'mga', 'armas', 'nga', 'nasakmit', 'gikan', 'sa', 'giingong', 'rebelde.', 'Walo', 'ka', 'armado', 'nga', 'gidudahang', 'sakop', 'sa', 'NPA', 'ang', 'nakigsukliay', 'og', 'bala', 'sa', 'mga', 'sakop', 'sa', '62nd', 'Infantry', 'Battalion', 'sa', 'Philippine', 'Army.', 'Nilungtad', 'og', '20', 'minutos', 'ang', 'maong', 'pinusilay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,063
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PINAKAGULANG', 'NGA', 'IRO', 'SA', 'KALIBUTAN', ',', 'GISAULOG', 'ANG', 'IYANG', 'IKA-31', 'KA', 'BIRTHDAY', 'Bag-ohay', 'lang', 'nga', 'nagsaulog', 'sa', 'iyang', 'ika-31', 'nga', 'birthday', 'ang', 'pinakagulang', 'nga', 'iro', 'sa', 'kalibutan', ',', 'matud', 'pa', 'sa', 'Guinness', 'Book', 'of', 'World', 'Records.', 'Gisaulog', 'sa', 'irong', 'si', 'Bobi', 'ang', 'iyang', 'birthday', 'niadtong', 'Sabado', 'atol', 'sa', 'usa', 'ka', 'party', 'sa', 'iyang', 'balay', 'sa', 'Conqueiros', ',', 'Portugal.', 'Usa', 'ka', 'purebred', 'nga', 'Rafeiro', 'do', 'Alentejo', 'ang', 'irong', 'si', 'Bobi', ',', 'usa', 'ka', 'lahi', 'sa', 'Portuguese', 'nga', 'iro.', 'Sumala', 'pa', 'sa', 'tag-iyang', 'si', 'Leonel', 'Costa', ',', 'gitambongan', 'sa', 'kapin', '100', 'ka', 'mga', 'bisita', 'ang', 'usa', 'ka', '"', 'very', 'traditional', '"', 'nga', 'party.', 'Dugang', 'pa', 'niya', ',', 'ang', 'hinungdan', 'sa', 'taas', 'nga', 'kinabuhi', 'ni', 'Bobi', 'mao', 'ang', '"', 'calm', ',', 'peaceful', 'environment', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,064
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPIN', '7-M', 'KA', 'MGA', 'PINOY', ',', 'MAKADAWAT', 'SA', 'INFLATION', 'AYUDA', 'Kapin', 'sa', 'pito', 'ka', 'milyon', 'nga', 'mga', 'Pilipino', 'ang', 'makabenepisyo', 'sa', 'programa', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', 'nga', 'Targeted', 'Cash', 'Transfer', '(', 'TCT', ')', '.', 'Sumala', 'pa', 'ni', 'DSWD', 'Assistant', 'Secretary', 'Romel', 'Lopez', ',', 'motabang', 'ang', 'ilang', 'departamento', 'sa', 'paghatag', 'sa', 'P500', 'nga', 'cash', 'assistance', 'matag', 'bulan', 'aron', 'pagtabang', 'sa', 'mga', 'benepisyaryo', 'niini', 'nga', 'makabawi', 'sa', 'pagsaka', 'sa', 'mga', 'presyo', 'sa', 'palaliton.', 'Bag-ohay', 'lang', ',', 'giaprobahan', 'sa', 'Department', 'of', 'Budget', 'and', 'Management', '(', 'DBM', ')', 'ang', 'Special', 'Allotment', 'Release', 'Order', 'sa', 'P7.68', 'bilyones', 'nga', 'pondo', 'alang', 'sa', 'TCT', 'program', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0]
|
cebuaner
|
4,065
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'nigradwar', 'sa', 'Negros', 'Oriental', 'State', 'University', '(', 'NORSU', ')', 'ang', 'nabutang', 'sa', 'Rank', '6', 'sa', 'March', '2023', 'Licensure', 'Exam', 'for', 'Professional', 'Teachers.', 'Matud', 'pa', 'kini', 'sa', 'gipagawas', 'nga', 'resulta', 'sa', 'Professional', 'Regulation', 'Commission', '(', 'PRC', ')', 'karong', 'adlawa', ',', 'May', '19', ',', '2023.', 'Nakuha', 'ni', 'Alvord', 'Van', 'Patten', 'Valencia', 'ang', '91.80', 'nga', 'passing', 'rate', 'sa', 'Secondary', 'Level', 'sa', 'naasoy', 'nga', 'exam', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,066
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hustisya', 'ang', 'gipanawagan', 'sa', 'mga', 'animal', 'lovers', 'alang', 'sa', 'duha', 'ka', 'iro', 'nga', 'gipusil', 'patay', 'sa', 'Metropolitan', 'police', 'sa', 'London', ',', 'United', 'Kingdom.', 'Ang', 'duha', 'ka', 'Staffordshire', 'bull', 'terrier', 'nga', 'sila', 'si', 'Marshall', 'ug', 'Millions', ',', 'gipanag-iya', 'sa', 'usa', 'ka', '46-anyos', 'nga', 'homeless', 'nga', 'si', 'Louie', 'Turnbull.', 'Sumala', 'pa', 'sa', 'report', ',', 'gipusil', 'sa', 'mga', 'pulis', 'ang', 'mga', 'iro', 'tungod', 'gapamaghot', 'kini', 'sa', 'usa', 'ka', 'babayi.', 'Samtang', 'si', 'Turnbull', ',', 'gidakop', 'sab', 'tungod', 'sa', 'kasong', 'pagpanag-iya', 'og', 'delikado', 'ug', ''out', 'of', 'control', ''', 'nga', 'mga', 'iro.', 'Sa', 'pahayag', 'ni', 'Turnbull', ',', 'giprotektahan', 'lamang', 'siya', 'sa', 'mga', 'iro', 'tungod', 'nihulbot', 'sa', 'ilang', 'pusil', 'ang', 'mga', 'pulis.', 'Ang', 'mga', 'dog', 'lovers', 'naghimo', 'og', 'petisyon', 'online', 'nga', 'moabot', 'na', 'ngadto', 'sa', '64,000', 'nga', 'pirma', 'alang', 'sa', 'pagpangayo', 'og', 'hustisya', 'sa', 'kamatayon', 'sa', 'maong', 'mga', 'iro', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 5, 6, 6, 6, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,067
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['5', 'KA', 'IRO', ',', 'PATAY', 'HUMAN', 'GIPANGHILOAN', 'Hustisya', 'ang', 'gisinggit', 'sa', 'pamilyang', 'Go', 'human', 'gihiloan', 'ang', 'ilang', 'lima', 'ka', 'iro', 'sa', 'wala', 'pa', 'mailhing', 'suspek.', 'Gisagop', 'sa', 'maong', 'pamilya', 'ang', 'mga', 'iro', 'nga', 'sila', 'si', 'Kobe', ',', 'Batman', ',', 'Bruce', ',', 'Kikay', ',', 'ug', 'Daya', 'sa', 'Guadalupe', ',', 'Cebu', 'City.', 'Apan', 'usa', 'ka', 'adlaw', ',', 'nakita', 'nalang', 'kini', 'nilang', 'wala', 'na'y', 'kinabuhi', 'ug', 'nagdugo', 'ang', 'baba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,068
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKING', 'HUBOG', ',', 'GIPUSIL', 'PATAY', 'SA', 'SAN', 'JOSE', 'Usa', 'ka', 'lalaking', 'gituohang', 'lango', 'sa', 'ilimnong', 'makahubog', 'ang', 'gipusil', 'patay', 'sa', 'Barangay', 'Basak', 'sa', 'San', 'Jose', 'mga', 'alas-6:10', 'sa', 'gabii', 'niadtong', 'Martes', ',', 'May', '16', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Roberto', 'Quilpo', ',', '55', 'anyos', ',', 'minyo', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Samtang', 'ang', 'suspek', 'giila', 'nga', 'si', 'Joseph', 'Quisel', 'Salapayni', ',', '27', 'anyos', ',', 'ulitawo', ',', 'ug', 'residente', 'sab', 'sa', 'maong', 'dapit.', 'Sumala', 'pa', 'sa', 'imbestigasyon', 'sa', 'kapulisan', ',', 'gituohang', 'hubog', 'ug', 'nag-amok', 'ang', 'biktima', 'ngadto', 'sa', 'panimalay', 'ni', 'Salapayni.', 'Giingong', 'gihulga', 'sab', 'niini', 'ang', 'pamilya', 'ni', 'Salapayni', 'gamit', 'ang', 'sundang.', 'Tungod', 'niini', ',', 'giingong', 'nibalos', 'si', 'Salapayni', 'pinaagi', 'sa', 'pagpusil', 'sa', 'biktima', 'gamit', 'ang', 'improvised', 'shotgun', 'kon', ''sulpak', ''', 'aron', 'depensahan', 'ang', 'iyang', 'kaugalingon', 'ug', 'ang', 'iyang', 'pamilya.', 'Human', 'sa', 'insidente', ',', 'gidakop', 'ang', 'suspek', 'sa', 'mga', 'nirespondeng', 'kapulisan', 'sa', 'San', 'Jose', 'ug', 'gidala', 'siya', 'sa', 'estasyon', 'alang', 'sa', 'tukmang', 'disposisyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,069
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['OFW', 'SA', 'HONG', 'KONG', ',', 'PATAY', 'HUMAN', 'MAHULOG', 'SA', 'BINTANA', 'SA', '18-FLOOR', 'BUILDING', 'Usa', 'ka', '38-anyos', 'nga', 'Overseas', 'Filipino', 'Worker', '(', 'OFW', ')', 'sa', 'Hong', 'Kong', 'ang', 'namatay', 'human', 'mahulog', 'sa', 'bintana', 'sa', '18-floor', 'apartment', 'nga', 'gilimpyohan', 'niini.', 'Mao', 'kini', 'ang', 'gikompirmar', 'sa', 'Philippine', 'Consulate', 'sa', 'Hong', 'Kong', 'niadtong', 'Martes', ',', 'May', '16', ',', '2023.', 'Sumala', 'pa', 'ni', 'Raly', 'Tejada', ',', 'consul', 'general', 'sa', 'Pilipinas', ',', 'nga', 'giimbestigaran', 'na', 'sa', 'kapulisan', 'sa', 'Hong', 'Kong', 'ang', 'maong', 'insidente.', 'Niadtong', '2017', ',', 'gidili', 'na', 'sa', 'Hong', 'Kong', 'ang', 'mga', 'amo', 'nga', 'mosugo', 'ngadto', 'sa', 'ilang', 'mga', 'katabang', 'sa', 'panimalay', 'sa', 'pagpanglimpyo', 'sa', 'mga', 'bintana', 'sa', 'tag-as', 'nga', 'mga', 'building', 'tungod', 'peligro', 'kini.', 'Nipasalig', 'sab', 'ang', 'gobyerno', 'sa', 'Pilipinas', 'nga', 'motabang', 'sa', 'pamilya', 'sa', 'namatay', 'nga', 'OFW', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,070
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HILABIHANG', 'KAINIT', 'SA', 'PANAHON', ',', 'GIBANABANANG', 'MOLUNGTAD', 'SA', 'SUNOD', 'NGA', '5', 'KA', 'TUIG', ':', 'UN', 'Sigurado', 'nga', 'ang', '2023', 'hangtod', '2027', 'mao', 'ang', 'pinakainit', 'nga', 'lima', 'ka', 'tuig', 'nga', 'natala', 'sukad', 'kaniadto.', 'Mao', 'kini', 'ang', 'gipasidaan', 'sa', 'United', 'Nations', 'niadtong', 'Miyerkules', ',', 'May', '17', ',', '2023.', 'Ilang', 'gipasabot', 'nga', 'ang', 'kombinasyon', 'sa', 'greenhouse', 'gases', 'ug', 'El', 'Niño', 'mao'y', 'hinungdan', 'sa', 'pagsaka', 'sa', 'temperatura.', 'Sumala', 'pa', 'sa', 'World', 'Meteorological', 'Organization', '(', 'WMO', ')', 'sa', 'UN', ',', 'posible', 'nga', 'usa', 'sa', 'mosunod', 'nga', 'lima', 'ka', 'tuig', 'ang', 'makasinati', 'og', 'global', 'temperatures', 'nga', 'molapas', 'sa', 'target', 'nga', 'gitakda', 'sa', 'Paris', 'sa', 'paglimite', 'sa', 'climate', 'change.', 'Natala', 'ang', 'hilabihang', 'kainit', 'nga', 'walo', 'ka', 'tuig', 'niadtong', '2015', 'hangtod', '2022', ',', 'diin', 'ang', '2016', 'mao', 'ang', 'pinakainit.', 'Apan', 'sumala', 'pa', 'sa', 'forecast', 'nga', 'mosaka', 'pa', 'ang', 'temperatura', 'tungod', 'sa', 'climate', 'change', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,071
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'NAMATAY', 'SA', 'BABOY', 'SA', 'DAUIN', ',', 'NEGATIBO', 'SA', 'AFRICAN', 'SWINE', 'FEVER', 'Negatibo', 'sa', 'African', 'Swine', 'Fever', '(', 'ASF', ')', 'ang', 'mga', 'namatay', 'nga', 'baboy', 'sa', 'Barangay', 'Maayong', 'Tubig', 'sa', 'Dauin.', 'Matod', 'pa', 'sa', 'gipagawas', 'nga', 'public', 'advisory', 'sa', 'Provincial', 'Veterinary', 'Office', 'karong', 'Miyerkules', ',', 'May', '17', ',', '2023.', 'Sumala', 'pa', 'sa', 'advisory', ',', 'aduna'y', 'mga', 'blood', 'sample', 'nga', 'gikolekta', 'gikan', 'sa', 'maong', 'mga', 'baboy', 'ug', 'gipadala', 'sa', 'Bureau', 'of', 'Animal', 'Industry', '-', 'Central', 'Office', 'ug', 'nigawas', 'nga', 'negatibo', 'kini', 'sa', 'ASF.', 'Nagpadayon', 'sab', 'ang', 'BAI', '-', 'Central', 'Office', 'sa', 'pagpahigayon', 'og', 'mga', 'test', 'sa', 'classical', 'swine', 'fever', '(', 'CSF', ')', 'ug', 'porcine', 'reproductive', 'and', 'respiratory', 'syndrome', '(', 'PRRS', ')', 'diin', 'wala', 'pa', 'matino', 'ang', 'resulta', 'niini.', 'Giawhag', 'sab', 'nila', 'ang', 'tanan', 'nga', 'mopahigayon', 'og', 'strikto', 'nga', 'biosafety', 'ug', 'biosecurity', 'measure', 'aron', 'mapunggan', 'ang', 'pagkuyanap', 'sa', 'bisan', 'unsang', 'swine', 'diseases', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 0, 0, 0, 7, 8, 8, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,072
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FOREIGNER', ',', 'NAPALGANG', 'PATAY', 'SA', 'BACONG', 'Napalgang', 'patay', 'ang', 'usa', 'ka', 'American', 'national', 'sa', 'giabangang', 'apartment', 'niini', 'sa', 'Purok', '4', ',', 'Barangay', 'Liptong', 'sa', 'Bacong', 'mga', '8:42', 'sa', 'buntag', 'niadtong', 'Martes', ',', 'May', '16', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'sa', 'Bacong', 'ang', 'foreigner', 'nga', 'si', 'Hunter', 'Brown', ',', '55', 'anyos', ',', 'ug', 'usa', 'ka', 'US', 'citizen.', 'Matod', 'pa', 'sa', 'kapulisan', 'nga', 'sa', 'ilang', 'pag-abot', 'sa', 'naasoy', 'nga', 'lugar', ',', 'napalgan', 'nilang', 'wala', 'nay', 'kinabuhi', 'ang', 'langyaw', 'nga', 'anaa', 'sa', 'salug', 'sa', 'iyang', 'giabangang', 'apartment.', 'Dali', 'sab', 'nga', 'niresponde', 'ang', 'mga', 'personahe', 'sa', 'SOCO.', 'Sa', 'imbestigasyon', ',', 'nasayran', 'nga', '"', 'natural', 'death', '"', 'ang', 'hinungdan', 'sa', 'kamatayon', 'sa', 'maong', 'foreigner', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,073
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LAING', 'ESTUDYANTE', 'SA', 'TANJAY', ',', 'NAKUYAPAN', 'TUNGOD', 'SA', 'GRABENG', 'KAINIT', 'Laing', 'estudyante', 'sa', 'Tanjay', 'National', 'High', 'School', '(', 'Opao', ')', 'ang', 'nakuyapan', 'sulod', 'sa', 'classroom', 'tungod', 'sa', 'grabeng', 'kainit', 'mga', 'pasado', 'alas-9:00', 'sa', 'buntag', 'karong', 'adlawa', ',', 'May', '17', ',', '2023.', 'Gikompirmar', 'sa', 'health', 'coordinator', 'sa', 'maong', 'eskwelahan', 'nga', 'heat', 'exhaustion', 'ang', 'hinungdan', 'sa', 'maong', 'panghitabo.', 'Kagahapong', 'adlawa', ',', 'aduna', 'sab', 'duha', 'ka', 'laing', 'estudyante', 'ang', 'gitabang', 'tungod', 'sa', 'kainit', 'sa', 'panahon', 'sa', 'naasoy', 'nga', 'tunghaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,074
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'usa', 'ka', 'video', ',', 'gihatagan', 'ni', 'Christian', 'Mar', 'ang', 'iyang', 'asawa', 'nga', 'si', 'Roviedelia', 'Soriano', 'Villenia', 'og', 'bouquet', 'nga', 'ginama', 'sa', 'kwartang', 'nagkantidad', 'og', 'P1', 'milyon', 'alang', 'sa', 'Mother', ''s', 'Day', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,075
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CONG.', 'TEVES', 'POSIBLENG', 'MOULI', 'SA', 'PILIPINAS', 'KARONG', 'MAYO', '17', ',', 'MATUD', 'PA', 'NI', 'DOJ', 'SEC.', 'REMULLA', 'Posibleng', 'mouli', 'sa', 'Pilipinas', 'si', 'Third', 'District', 'Rep.', 'Arnolfo', '"', 'Arnie', '"', 'Teves', 'Jr.', ',', 'karong', 'Miyerkules', ',', 'Mayo', '17', ',', '2023', ',', 'sumala', 'pa', 'ni', 'Justice', 'Secretary', 'Jesus', 'Crispin', 'Remulla.', 'Gibutyag', 'kini', 'ni', 'Remulla', 'human', 'siya', 'niingon', 'nga', 'duna', 'siyay', 'masaligang', 'tinubdan', 'nga', 'nasayod', 'sa', 'flight', 'operations.', 'Gawas', 'niini', ',', 'giingon', 'sab', 'ni', 'Remulla', 'nga', 'gilaumang', 'pasakahan', 'na', 'ang', 'mga', 'reklamo', 'batok', 'ni', 'Teves', 'Miyerkules', 'sa', 'udto.', 'Nakaalerto', 'na', 'sab', 'ang', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'sa', 'posibleng', 'pag-uli', 'ni', 'Teves', 'sa', 'nasud', ',', 'matud', 'pa', 'ni', 'Remulla.', 'Ang', 'maong', 'kalambuan', 'gibutyag', 'dul-an', 'usa', 'ka', 'semana', 'human', 'napakyas', 'si', 'Teves', 'pagkuha', 'og', 'asylum', 'didto', 'sa', 'Timor-Leste.', 'Apan', 'kini', 'gihimakak', 'sa', 'abogado', 'ni', 'Teves', 'nga', 'si', 'Ferdinand', 'Topacio.', 'Gani', 'gitawag', 'niya', 'kini', 'nga', '“fake', 'news.”', 'Una', 'nang', 'gihimakak', 'sa', 'kongresista', 'nga', 'dunay', 'kalambigitan', 'ang', 'iyang', 'banay', 'sa', 'pagpatay', 'kang', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'niadtong', 'Marso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 5, 6, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0]
|
cebuaner
|
4,076
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KA', 'ESTUDYANTE', 'SA', 'TANJAY', ',', 'GITABANG', 'TUNGOD', 'SA', 'GRABENG', 'KAINIT', 'Duha', 'ka', 'mga', 'estudyante', 'sa', 'Tanjay', 'National', 'High', 'School', '(', 'Opao', ')', 'ang', 'gitabang', 'sa', 'mga', 'magtutudlo', 'tungod', 'sa', 'grabeng', 'kaiinit', 'sa', 'panahon.', 'Ang', 'duha', 'ka', 'estudayante', 'puros', '15', 'anyos', 'ug', 'Grade', '9', 'students', 'sa', 'nahisgutang', 'tulunghaan.', 'Nahibaloan', 'nga', 'ang', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'wala'y', 'plano', 'nga', 'ibalik', 'ang', '"', 'summer', 'break', '"', 'ngadto', 'sa', 'Abril', 'ug', 'Mayo', ',', 'taliwala', 'sa', 'mga', 'reklamo', 'gikan', 'sa', 'mga', 'magtutudlo', 'ug', 'estudyante', 'nga', 'giingong', 'naglisod', 'sa', 'klase', 'tungod', 'sa', 'hilabihang', 'kainit.', 'Apan', ',', 'gitun-an', 'na', 'karon', 'sa', 'gobyerno', 'ang', 'posibleng', 'pagbalik', 'sa', '"', 'summer', 'break', '"', 'sa', 'naandang', 'panahon', 'niini', 'gikan', 'sa', 'Marso', 'hangtud', 'sa', 'Mayo', ',', 'sumala', 'pa', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0]
|
cebuaner
|
4,077
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SENADOR', ',', 'GISUGYOT', 'NGA', 'MAHIMONG', 'REQUIRED', 'ANG', '2ND', 'BOOSTER', 'BATOK', 'COVID-19', 'Gisugyot', 'ni', 'Sen.', 'Francis', 'Tolentino', 'nga', 'himuong', 'required', 'ang', 'ikaduhang', 'booster', 'sa', 'bakuna', 'batok', 'Covid-19.', 'Gisubli', 'ni', 'Tolentino', 'nga', 'girekomendar', 'sa', 'mga', 'health', 'professionals', 'ang', 'ikaduhang', 'booster', 'shot', 'tungod', 'makatabang', 'kini', 'sa', 'pagpanalipod', 'batok', 'Covid-19', 'ug', 'dugang', 'nga', 'resistensya', 'ug', 'garantiya', 'nga', 'dili', 'mataptan', 'sa', 'maong', 'virus.', 'Sumala', 'pa', 'sa', 'datos', 'Department', 'of', 'Health', '(', 'DOH', ')', ',', 'anaa', 'sa', '179,046,746', 'ang', 'kinatibuk-ang', 'covid', 'shots', 'nga', 'nahatag', 'sa', 'populasyon', ',', 'samtang', 'anaa', 'sa', '24,178,325', 'ang', 'booster', 'doses', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,078
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE-SIBULAN', 'AIRPORT', ',', 'IKA-15', 'SA', ''BUSIEST', 'AIRPORTS', ''', 'SA', 'PH', 'Nahimutang', 'ang', 'Dumaguete-Sibulan', 'Airport', 'sa', 'ika-15', 'nga', 'ranggo', 'sa', ''busiest', 'airports', ''', 'sa', 'Pilipinas', ',', 'matod', 'pa', 'sa', 'gipagawas', 'nga', 'datos', 'sa', 'Civil', 'Aviation', 'Authority', 'in', 'the', 'Philippines', '(', 'CAAP', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0]
|
cebuaner
|
4,079
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DBM', ':', 'TRABAHANTE', 'SA', 'GOBYERNO', ',', 'MAKADAWAT', 'SA', 'ILANG', 'MIDYEAR', 'BONUS', 'SUGOD', 'SA', 'MAY', '15', 'Makadawat', 'sa', 'ilang', 'midyear', 'bonus', 'ang', 'mga', 'kwalipikadong', 'trabahante', 'sa', 'gobyerno', 'sugod', 'sa', 'Lunes', ',', 'May', '15', ',', '2023.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Department', 'of', 'Budget', 'and', 'Management', '(', 'DBM', ')', 'niadtong', 'Dominggo', ',', 'May', '14', ',', '2023.', 'Giawhag', 'ni', 'Budget', 'Secretary', 'Amenah', 'Pangandaman', 'ang', 'tanang', 'ahensya', 'ug', 'opisina', 'sa', 'gobyerno', 'sa', 'paghatag', 'sa', 'maong', 'bonus', 'sa', 'insaktong', 'oras.', 'Sumala', 'pa', 'sa', 'DBM', ',', 'ang', 'midyear', 'bonus', 'kay', 'katumbas', 'sa', 'usa', 'ka', 'bulan', 'nga', 'basic', 'pay', 'sa', 'usa', 'ka', 'empleyado', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,080
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FDA', ',', 'GI-BAN', 'ANG', 'PAGPASULOD', 'OG', ''PROCESSED', 'PORK', ''', 'GIKAN', 'SA', 'SINGAPORE', 'TUNGOD', 'SA', 'ASF', 'Gimando', 'sa', 'Food', 'and', 'Drug', 'Administration', '(', 'FDA', ')', 'ang', 'temporaryong', 'pagdili', 'sa', 'pagpasulod', 'sa', 'nasud', 'sa', 'tanang', ''processed', 'pork', 'meat', 'products', ''', 'gikan', 'sa', 'Singapore.', 'Sumala', 'pa', 'sa', 'FDA', ',', 'ang', 'Singapore', 'ang', 'ika-30', 'nga', 'nasud', 'nga', 'gidili', 'sa', 'Pilipinas', 'sa', 'pagpasulod', 'og', 'mga', ''processed', 'pork', 'meat', 'products', ''', 'tungod', 'sa', 'African', 'swine', 'fever', '(', 'ASF', ')', '.', 'Gawas', 'sa', 'Singapore', ',', 'lakip', 'sab', 'sa', 'mga', 'niaging', 'mando', 'sa', 'FDA', 'ang', 'nasud', 'sa', 'China', ',', 'Hungary', ',', 'Latvia', ',', 'Poland', ',', 'Romania', ',', 'Russia', ',', 'Ukraine', ',', 'Vietnam', ',', 'Zambia', ',', 'South', 'Africa', ',', 'Bulgaria', ',', 'Cambodia', ',', 'Mongolia', ',', 'Moldova', 'ug', 'Hong', 'Kong', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 7, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0]
|
cebuaner
|
4,081
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'swerte', 'nga', 'tigpusta', 'ang', 'nakadaog', 'sa', 'kapin', 'P225', 'milyon', 'nga', 'jackpot', 'prize', 'sa', 'Mega', 'Lotto', '6', '/', '45', 'sa', 'draw', 'niini', 'niadtong', 'Biyernes', ',', 'May', '12', ',', '2023.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Charity', 'Sweepstakes', 'Office', '(', 'PCSO', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0]
|
cebuaner
|
4,082
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'mga', 'kalihokan', 'sa', 'Dumaguete', 'Triathlon', 'nga', 'apilan', 'sa', 'dul-an', '400', 'ka', 'triathletes', 'gikan', 'sa', 'tibuok', 'nasud.', 'Ipahigayon', 'kini', 'sa', 'Pantawan', ',', 'Rizal', 'Boulevard', 'karong', 'Dominggo', ',', 'May', '14', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,083
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KONGKRETONG', 'TAYTAYAN', 'SA', 'BATINGUEL-JUNOB', ',', 'SUGDAN', 'NA', 'PAGTUKOD', 'KARONG', 'BULANA', 'Sugdan', 'na', 'karong', 'bulana', 'ang', 'pagtukod', 'sa', 'usa', 'ka', 'kongkretong', 'taytayan', 'nga', 'magsumpay', 'sa', 'Barangay', 'Batinguel', 'ug', 'Junob', 'sa', 'dakbayan', 'sa', 'Dumaguete.', 'Tumong', 'sa', 'maong', 'proyekto', 'nga', 'mailisan', 'ang', 'karaang', 'footbridge', 'niini', 'aron', 'masiguro', 'nga', 'luwas', 'ang', 'pagbiyahe', 'sa', 'mga', 'motorista', 'ug', 'makatabang', 'sa', 'pagmenus', 'sa', 'trapiko', 'sa', 'dakbayan.', 'Sumala', 'pa', 'ni', 'Mayor', 'Felipe', 'Remollo', ',', 'lapad', 'ug', 'taas', 'ang', 'himuong', 'bag-ong', 'taytayan', 'aron', 'dili', 'malapawan', 'sa', 'tubig', 'kon', 'mobaha.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'ang', 'DPWH', 'sa', 'pagtukod', 'og', 'river', 'control', 'dike', 'sa', 'naasoy', 'nga', 'lugar.', 'Karong', 'tuiga', ',', 'gilaomang', 'sugdan', 'na', 'sab', 'sa', 'pagtukod', 'ang', 'taytayan', 'nga', 'magsumpya', 'sa', 'Barangay', 'Candau-ay', 'ug', 'Balugo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0]
|
cebuaner
|
4,084
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['EL', 'NIÑO', ',', 'GIPANGANDAMAN', 'NA', 'SA', 'DUMAGUETE', 'CITY', 'Nagpahigayon', 'ang', 'dakbayan', 'sa', 'Dumaguete', 'og', 'massive', 'information', 'drive', 'aron', 'maandam', 'ang', 'mga', 'responsableng', 'opisyal', 'ug', 'residente', 'batok', 'sa', 'negatibo', 'nga', 'epekto', 'sa', 'El', 'Niño', 'phenomenon.', 'Sumala', 'pa', 'sa', 'PAGASA', ',', 'magsugod', 'ang', 'El', 'Niño', 'phenomenon', 'sa', 'Hunyo-Agosto', 'ug', 'may', 'posibilidad', 'nga', 'magpadayon', 'hangtod', 'sa', 'unang', 'quarter', 'sa', '2024.', 'Giandam', 'na', 'sab', 'ang', 'mga', 'sektor', 'sa', 'agrikultura', ',', 'water', 'resources', ',', 'marine', 'resources', ',', 'ug', 'human', 'health', 'alang', 'umalabot', 'nga', 'El', 'Niño.', 'Tumong', 'sa', 'dakbayan', 'nga', 'ipatuman', 'sa', 'mga', 'residente', 'ang', 'water', 'conservation', 'measures', ',', 'pagpahigayon', 'og', 'information', 'education', 'sa', 'kabarangayan', ',', 'paghatag', 'ug', 'relief', 'goods', ',', 'tambal', 'ug', 'medical', 'personnel', 'alang', 'sa', 'bisan', 'unsang', 'emerhensya', 'ug', 'paggamit', 'sa', 'LDRRM', 'fund', 'alang', 'sa', 'gikinahanglang', 'galastohan', 'sa', 'maong', 'phenomenon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 8, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 3, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,085
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['5', 'ANYOS', 'NGA', 'BATA', ',', 'PATAY', 'HUMAN', 'NAKAKAON', 'OG', 'BAKI', ';', '4', 'KA', 'IGSUON', ',', 'NAOSPITAL', 'Patay', 'ang', 'usa', 'ka', '5', 'anyos', 'nga', 'batang', 'lalaki', 'human', 'makakaon', 'og', 'sinugba', 'nga', 'baki', 'ug', 'linuto', 'nga', 'balanghoy', 'sa', 'Purok', 'Piña', ',', 'Barangay', 'Dicañas', 'sa', 'Dipolog', 'City', 'niadtong', 'Miyerkules', ',', 'May', '10', ',', '2023.', 'Gikompirmar', 'sab', 'sa', 'Dipolog', 'City', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Office', 'nga', 'anaa', 'ang', 'upat', 'ka', 'igsuon', 'niini', 'sa', 'ospital', 'tungod', 'sa', 'food', 'poisoning.', 'Sumala', 'pa', 'sa', 'report', ',', 'puros', 'walay', 'trabaho', 'ang', 'ginikanan', 'sa', 'maong', 'mga', 'bata', 'ug', 'nagpuyo', 'sila', 'sa', 'usa', 'ka', 'barongbarong', 'sa', 'hilit', 'nga', 'dapit', 'sa', 'naasoy', 'nga', 'lugar.', 'Nipasalig', 'ang', 'opisina', 'ni', 'Dipolog', 'Mayor', 'Darel', 'Uy', 'nga', 'mohatag', 'og', 'suporta', 'sa', 'upat', 'ka', 'sakop', 'sa', 'maong', 'pamilya', 'nga', 'anaa', 'sa', 'ospital', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,086
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'SA', 'AYUNGON', ',', 'PATAY', 'HUMAN', 'MADASMAGAN', 'OG', 'SAKYANAN', 'NGA', 'GIMANEHO', 'OG', 'PULIS', 'Patay', 'ang', 'usa', 'ka', 'lalaki', 'human', 'siya', 'nilabang', 'sa', 'kalsada', 'ug', 'nadasmagan', 'sa', 'usa', 'ka', 'sakyanan', 'sa', 'Barangay', 'Awaan', 'sa', 'lungsod', 'sa', 'Ayungon', 'mga', 'alas-7:00', 'sa', 'gabii', 'niadtong', 'May', '9', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Haide', 'Jumuad', ',', 'hingkod', 'ang', 'pangidaron', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Samtang', 'ang', 'sakyanan', 'nga', 'nakadasmag', ',', 'gimaneho', 'ni', 'PCPT', 'Rexel', 'Yosores', 'Soreño', ',', 'kinsa', 'nakadestino', 'sa', 'Cebu', 'City', 'Police', 'Office.', 'Sumala', 'pa', 'sa', 'imbestigasyon', ',', 'samtang', 'nagbiyahe', 'si', 'Soreño', 'paingon', 'sa', 'Dumaguete', 'City', ',', 'nisulay', 'pagtabok', 'sa', 'kalsada', 'ang', 'biktima', 'apan', 'naigo', 'kini', 'sa', 'sakyanan.', 'Gidala', 'ang', 'biktima', 'sa', 'Bindoy', 'District', 'Hospital', 'apan', 'gideklarar', 'kining', 'dead', 'on', 'arrival', 'sa', 'nag-atiman', 'nga', 'doktor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,087
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SU', ',', 'MAGTANOM', 'OG', '500,000', 'KA', 'MANGROVE', 'TREES', 'SA', 'HABAGATANG', 'NEGOR', 'Nakig-partner', 'ang', 'Silliman', 'University', '(', 'SU', ')', 'sa', 'GCash', 'ug', 'United', 'States', 'International', 'Agency', 'for', 'Development', '(', 'USAID', ')', 'Fish', 'Right', 'Program', 'aron', 'pagtanom', 'og', 'tunga', 'sa', 'milyon', 'nga', 'mangrove', 'trees', 'sa', 'Negros', 'Oriental.', 'Tumong', 'niini', 'ang', 'pakigbatok', 'sa', 'climate', 'change', 'ug', 'pagsuporta', 'sa', 'biodiversity.', 'Giila', 'sa', 'maong', 'panag-uban', 'ang', 'importansiya', 'sa', 'mangrove', 'ecosystems', 'sa', 'climate', 'change', 'mitigation', 'and', 'adaptation', 'ug', 'pagsuporta', 'sa', 'panginabuhi', 'sa', 'komunidad', 'nga', 'anaa', 'sa', 'kabaybayonan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 3, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,088
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', ''colorized', ''', 'nga', 'litrato', 'sa', 'mga', 'estudyante', 'sa', 'Negros', 'Oriental', 'Provincial', 'High', 'School', 'nga', 'naggama', 'og', 'mga', 'muwebles', 'atol', 'sa', 'ilang', 'klase', 'niadtong', '1920', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,089
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SENADO', ',', 'GIAWHAG', 'ANG', 'COMELEC', 'NGA', 'IPAUBOS', 'ANG', 'NEGOR', 'SA', 'KONTROL', 'NIINI', 'Giawhag', 'sa', 'usa', 'ka', 'panel', 'sa', 'Senado', 'ang', 'Commission', 'on', 'Elections', '(', 'Comelec', ')', 'nga', 'ipaubos', 'ang', 'Negros', 'Oriental', 'sa', 'kontrol', 'niini', 'alang', 'sa', 'umalabot', 'nga', 'piniliay', 'sa', 'Barangay', 'ug', 'Sangguniang', 'Kabataan', '(', 'SK', ')', 'karong', 'Oktubre.', 'Ilang', 'gikutlo', 'ang', 'mga', 'kabalaka', 'bahin', 'sa', 'kalinaw', 'ug', 'kahusay', 'nga', 'kahimtang', 'sa', 'probinsya', 'human', 'sa', 'pagpatay', 'kang', 'Gov.', 'Roel', 'Degamo', 'ug', 'uban', 'pang', 'biktima.', 'Sumala', 'pa', 'ni', 'Comelec', 'chair', 'George', 'Erwin', 'Garcia', ',', 'nagdepende', 'sa', 'rekomendasyon', 'sa', 'Armed', 'Forces', 'of', 'the', 'Philippines', '(', 'AFP', ')', 'ug', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'ang', 'desisyon', 'kung', 'ipaubos', 'ba', 'o', 'dili', 'ang', 'Negros', 'Oriental', 'sa', 'kontrol', 'sa', 'Comelec', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 3, 0]
|
cebuaner
|
4,090
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibanabanang', 'moabot', 'ngadto', 'sa', 'kapin', 'P55', 'milyones', 'ang', 'danyos', 'sa', 'nahitabong', 'sunog', 'sa', 'usa', 'ka', 'bodega', 'sa', 'Barangay', 'Bantayan', ',', 'Dumaguete', 'City', 'Martes', 'sa', 'udto', ',', 'May', '9', ',', '2023.', 'Sumala', 'pa', 'sa', 'Dumaguete', 'City', 'Fire', 'Station', ',', 'nilungtad', 'og', 'dul-an', '12', 'oras', 'ang', 'maong', 'sunog', 'ug', 'napalong', 'nila', 'kini', 'alas-12:25', 'na', 'sa', 'kaadlawon', 'Miyerkules.', 'Sa', 'pagkakaron', ',', 'padayon', 'pang', 'giimbestigaran', 'ang', 'hinungdan', 'sa', 'maong', 'sunog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,091
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['P1-M', 'CASH', 'GIFT', 'BILL', 'ALANG', 'SA', 'MGA', 'PINOY', 'CENTENARIAN', ',', 'APRUBADO', 'NA', 'SA', 'KAMARA', 'Amendaran', 'sa', 'House', 'Bill', '7535', 'ang', 'Centenarian', 'Law', '(', 'Republic', 'Act', '10868', ')', 'nga', 'imbes', 'P100,000', ',', 'makadawat', 'na', 'og', 'P1', 'milyon', 'ang', 'swerte', 'nga', 'Filipino', 'centenarians.', 'Nakakuha', 'ang', 'HB', '7535', 'og', '257', 'ka', 'boto', 'nga', 'pabor', 'ug', 'wala'y', 'bisan', 'usa', 'ka', 'magbabalaod', 'ang', 'misupak', 'niini', 'atol', 'sa', 'plenaryo.', 'Ubos', 'sa', 'maong', 'sugyot', ',', 'P1', 'milyon', 'ang', 'ihatag', 'sa', 'mga', 'Filipino', 'nga', 'moabot', 'sa', 'edad', 'na', '101', 'sa', 'Pilipinas', 'man', 'o', 'sa', 'gawas', 'sa', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 3, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,092
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JACKPOT', 'SA', 'MEGA', 'LOTTO', '6', '/', '45', ',', 'GILAOMANG', 'MOABOT', 'SA', 'KAPIN', 'P200-M', 'Gilaomang', 'moabot', 'ngadto', 'sa', 'kapin', 'P200', 'milyones', 'ang', 'jackpot', 'price', 'sa', '6', '/', '45', 'Mega', 'Lotto', 'karong', 'Miyerkules.', 'Sumala', 'pa', 'sa', 'Philippine', 'Charity', 'Sweepstakes', 'Office', '(', 'PCSO', ')', ',', 'wala'y', 'nakakuha', 'sa', 'winning', 'combination', 'nga', '04', '-44-25-40-26-42', 'niadtong', 'Lunes', 'sa', 'gabii', 'nga', 'aduna'y', 'premyo', 'nga', 'P196.06', 'milyones.', 'Tungod', 'niini', ',', 'mosaka', 'ngadto', 'sa', 'P207', 'milyones', 'ang', 'jackpot', 'estimate', 'alang', 'sa', 'draw', 'karong', 'Miyerkules.', 'Gidula', 'ang', 'Mega', 'Lotto', 'matag', 'Lunes', ',', 'Miyerkules', 'ug', 'Biyernes.', 'Wala', 'pa', 'sab', 'nakadaog', 'sa', 'P29.7', 'milyones', 'nga', 'jackpot', 'sa', '6', '/', '55', 'Grand', 'Lotto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0]
|
cebuaner
|
4,093
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', 'CITY', 'POLICE', 'STATION', ',', 'GIILA', 'ISIP', 'TOP', 'POLICE', 'STATION', 'SA', 'NEGOR', 'Gihatagan', 'ang', 'Dumaguete', 'City', 'Police', 'Station', 'og', 'Certificate', 'of', 'Commendation', 'human', 'sila', 'nabutang', 'sa', 'rank', 'Number', '1', 'sa', 'tanang', 'City', 'Police', 'Station', 'sa', 'Negros', 'Oriental', 'Police', 'Provincial', 'Office.', 'Nakuha', 'sa', 'Dumaguete', 'PNP', 'ang', 'kinatibuk-ang', '135.8', 'points', 'sa', 'Four-Focus', 'Operations', 'alang', 'sa', 'bulan', 'sa', 'April', 'karong', 'tuiga.', 'Ang', 'maong', 'mga', 'operasyon', 'mao', 'ang', 'Anti-Illegal', 'Drugs', ',', 'Anti-Illegal', 'Gambling', ',', 'Wanted', 'Persons', ',', 'ug', 'Loose', 'Firearms.', 'Nagpakita', 'ang', 'maong', 'pasiugda', 'sa', 'dedikasyon', 'ug', 'pasalig', 'sa', 'Dumaguete', 'PNP', 'sa', 'pagsiguro', 'sa', 'kaluwasan', 'ug', 'kaayuhan', 'sa', 'komunidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 6, 6, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,094
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Padayong', 'gipalong', 'karon', 'sa', 'kabumberohan', 'ang', 'sunog', 'nga', 'niulbo', 'sa', 'usa', 'ka', 'bodega', 'sa', 'Barangay', 'Bantayan', ',', 'Dumaguete', 'City.', 'Gikatahong', 'niabot', 'na', 'sa', 'ikatulong', 'alarma', 'ang', 'maong', 'sunog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,095
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LAING', 'BIKTIMA', 'NGA', 'NASAMDAN', 'SA', 'PAMPLONA', 'MASSACRE', ',', 'NAMATAY', 'NIADTONG', 'WEEKEND', 'Nisaka', 'na', 'ngadto', 'sa', 'pulo', 'ang', 'mga', 'namatay', 'sa', 'Pamplona', 'Massacre', 'nga', 'nahitabo', 'niadtong', 'Mar.', '4', ',', '2023', 'human', 'usa', 'ka', 'samdan', 'nga', 'biktima', 'ang', 'namatay', 'niadtong', 'Dominggo.', 'Sa', 'usa', 'ka', 'Facebook', 'post', 'ni', 'Mayor', 'Janice', 'Vallega', 'Degamo', ',', 'giila', 'ang', 'bag-ong', 'namatay', 'nga', 'si', 'Fredilino', 'Cafe', 'Jr.', ',', 'kinsa', 'nailhan', 'sab', 'sa', 'pangalan', 'nga', '"', 'Putok.', '"', 'Namatay', 'si', 'Cafe', 'tungod', 'sa', 'iyang', 'mga', 'samad', 'sa', 'kalawasan', 'nga', 'iyang', 'naangkon', 'atol', 'sa', 'pagpamusil', 'sa', 'mga', 'armadong', 'lalaki', 'sa', 'pribadong', 'compound', 'ni', 'Degamo', 'sa', 'Pamplona.', 'Lumad', 'nga', 'taga', 'habagatang', 'habig', 'sa', 'lungsod', 'sa', 'Santa', 'Catalina', 'si', 'Cafe', 'ug', 'usa', 'ka', 'empleyado', 'sa', 'Negros', 'Oriental', 'Provinical', 'Engineering', 'Office.', 'Usa', 'siya', 'sa', 'mga', 'kanunay', 'nga', 'nag-uban', 'ni', 'Degamo', 'atol', 'sa', 'konsultasyon', 'kada', 'katapusan', 'sa', 'compound', 'niini', 'sa', 'Pamplona.', 'Sa', 'niaging', 'bulan', ',', 'nagpahigayon', 'ang', 'Senate', 'Committee', 'on', 'Public', 'Order', 'and', 'Dangerous', 'Drugs', 'og', 'mga', 'hearing', 'sa', 'pagpatay', 'kang', 'Degamo', 'ug', 'uban', 'pang', 'sunodsunod', 'nga', 'pagpatay', 'sa', 'probinsya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,096
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'pa', 'sa', 'gipagawas', 'nga', 'datos', 'sa', 'PAGASA', ',', 'niabot', 'ngadto', 'sa', '40°C', 'ang', 'heat', 'index', 'sa', 'Dumaguete', 'City', 'niadtong', 'Dominggo', ',', 'May', '7', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,097
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sunog', 'niulbo', 'sa', 'karaang', 'Provincial', 'Capitol', 'Building', 'sa', 'Siquijor', 'sa', 'lungsod', 'sa', 'Larena', 'karong', 'Domingo', ',', 'Mayo', '7', ',', '2023.', 'Sa', 'pagkakaron', ',', 'nagpahigayon', 'pa', 'og', 'clearing', 'operations', 'ang', 'kabumberohan', 'didto.', 'Gikatahong', 'dunay', 'mga', 'opisina', 'sa', 'gobyerno', 'ang', 'anaa', 'gihapon', 'sa', 'karaang', 'kapitolyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 5, 6, 6, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,098
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['COVID-19', ',', 'DILI', 'NA', 'USA', 'KA', 'GLOBAL', 'HEALTH', 'EMERGENCY', 'MATUD', 'PA', 'SA', 'WHO', 'Human', 'ang', 'kapin', '3', 'ka', 'tuig', 'nga', 'nagkatap', 'kini', 'sa', 'kalibutan', 'ug', 'nakapatay', 'og', 'dili', 'momenos', 'sa', '20', 'milyon', 'ka', 'tawo', ',', 'gibakwi', 'na', 'sa', 'World', 'Health', 'Organization', '(', 'WHO', ')', 'ang', 'estado', 'sa', 'COVID-19', 'isip', 'usa', 'ka', 'global', 'health', 'emergency.', 'Atol', 'sa', 'panagtigom', 'sa', 'usa', 'ka', 'independent', 'committee', 'sa', 'WHO', 'niadtong', 'Huwebes', ',', 'nauyonan', 'nga', 'wala', 'na', 'sa', 'highest', 'level', 'of', 'alert', 'ang', 'COVID-19', 'crisis.', 'Apan', 'giklaro', 'sa', 'pangulo', 'sa', 'WHO', 'nga', 'si', 'Tedros', 'Adhanom', 'Ghebreyesus', 'nga', 'nagpabilin', 'gihapon', 'ang', 'peligro', 'sa', 'maong', 'sakit', 'bisan', 'pa', 'ug', 'dili', 'na', 'kini', 'global', 'health', 'emergency.', 'Mahinumduman', 'nga', 'giisip', 'nga', 'Public', 'Health', 'Emergency', 'of', 'International', 'Concern', 'kon', '(', 'PHEIC', ')', 'ang', 'COVID-19', 'niadtong', 'Enero', '30', ',', '2020.', 'Niadtong', 'Marso', 'sa', 'susamang', 'tuig', ',', 'gideklarar', 'ang', 'COVID-19', 'isip', 'usa', 'ka', 'pandemya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0]
|
cebuaner
|
4,099
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MOTORISTA', ',', 'PATAY', 'SA', 'USA', 'KA', 'ROAD', 'ACCIDENT', 'SA', 'TANJAY', 'CITY', 'Patay', 'ang', 'usa', 'ka', 'lalaki', 'human', 'sa', 'usa', 'ka', 'road', 'accident', 'sa', 'Barangay', 'Manipis', 'sa', 'Tanjay', 'City', 'niadtong', 'May', '4', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Nelson', 'Catacutan', 'Acebo', 'Jr.', ',', '22', 'anyos', ',', 'ug', 'lumolupyo', 'sa', 'Barangay', '4', 'sa', 'naasoy', 'nga', 'syudad.', 'Gimaneho', 'niini', 'ang', 'usa', 'ka', 'Suzuki', 'Smash.', 'Ang', 'ka', 'engkwetro', 'niini', ',', 'usa', 'ka', 'Isuzu', 'truck', 'nga', 'gimaneho', 'ni', 'Medes', 'Su-od', 'Pahamtang', ',', '35', 'anyos', ',', 'ug', 'residente', 'sa', 'Purok', 'Mangga', ',', 'Barangay', 'Magsusunog', 'sa', 'lungsod', 'sa', 'Pamplona.', 'Sumala', 'pa', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'nasayran', 'nga', 'nigawas', 'sa', 'linya', 'ang', 'motorsiklo', 'pag-abot', 'niini', 'sa', 'crossing', 'sa', 'nahitaboan', 'sa', 'aksidente.', 'Samtang', 'ang', 'truck', 'nga', 'nagbiyahe', 'sa', 'pikas', 'direksyon', ',', 'naigo', 'ang', 'likod', 'nga', 'bahin', 'sa', 'motorsiklo', 'bisan', 'pa', 'sa', 'pagsulay', 'niini', 'sa', 'pagliko', 'aron', 'malikayan', 'ang', 'pagkabangga.', 'Tungod', 'niini', ',', 'nakaangkon', 'og', 'samad', 'sa', 'kalawasan', 'ang', 'biktima', 'hinungdan', 'sa', 'iyang', 'hinanaling', 'kamatayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
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