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4,100
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAESTRA', ',', 'NASAKPAN', 'NI', 'MISTER', 'NGA', 'NAGLABING-LABING', 'SA', 'LAING', 'LALAKI', 'Usa', 'ka', 'magtutudlo', 'sa', 'public', 'school', 'ug', 'giingong', 'kabit', 'niini', 'ang', 'nasakpan', 'sa', 'akto', 'sa', 'iyang', 'bana', 'nga', 'naglabing-labing', 'mga', 'alas', '9:50', 'sa', 'gabii', 'niadtong', 'Mayo', '3', ',', '2023.', 'Giila', 'ang', 'maestra', 'sa', 'alyas', 'nga', '"', 'Teacher', 'Lovely', ',', '"', '26', 'anyos', ',', 'ug', 'lumulupyo', 'sa', 'lungsod', 'sa', 'Guihulngan.', 'Samtang', 'ang', 'iyang', 'kalabing-labning', 'mao', 'si', 'alyas', '"', 'Boy', 'Tisoy', ',', '"', '30', 'anyos', ',', 'ug', 'residente', 'sa', 'Sitio', 'Bucana', ',', 'Barangay', 'Poblacion', 'sa', 'naasoy', 'nga', 'lungsod.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'kapulisan', ',', 'miadto', 'sa', 'kapulisan', 'ang', 'bana', 'sa', 'maong', 'magtutudlo', 'kinsa', 'usa', 'ka', 'backhoe', 'operator', 'aron', 'mangayo', 'og', 'tabang', 'bahin', 'sa', 'usa', 'ka', '"', 'kadudahang', 'tawo', '"', 'nga', 'anaa', 'sulod', 'sa', 'ilang', 'panimalay.', 'Subay', 'niini', ',', 'sa', 'pag-abot', 'sa', 'responding', 'team', ',', 'giingong', 'nasakpan', 'sa', 'akto', 'ang', 'magtutudlo', 'uban', 'sa', 'iyang', 'kabit', 'nga', 'anaa', 'sulod', 'sa', 'ilang', 'kwarto', 'nga', 'gabuhat', 'sa', 'lawasnong', 'pakiglantugi', 'hinungdan', 'nga', 'ilang', 'pagkasikop.', 'Dugang', 'pa', ',', 'gipakita', 'sa', 'bana', 'ang', 'usa', 'ka', 'video', 'pagpamatuod', 'nga', 'ang', 'duha', 'ka', 'mga', 'suspetsado', 'aduna', 'kini', 'relasyon.', 'Kasong', 'adultery', 'ubos', 'sa', 'Article', '333', 'sa', 'RPC', 'ang', 'giandam', 'na', 'alang', 'sa', 'mga', 'suspek', '.'] 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
|
4,101
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpadayon', 'ang', 'Materials', 'Recovery', 'Facility', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'sa', 'pagprodyus', 'og', 'plastic', 'nga', 'mga', 'lingkuranan', ',', 'construction', 'materials', 'ug', 'uban', 'pang', 'mapuslanong', 'byproducts', 'nga', 'nakuha', 'gikan', 'sa', 'basura', 'nga', 'makolekta', 'matag', 'adlaw', 'gikan', 'sa', 'mga', 'business', 'establishment', 'ug', 'residente', 'sa', 'tanang', '30', 'ka', 'component', 'barangays.', 'Gipaninguha', 'sa', 'dakbayan', 'nga', 'ma-prayoridad', 'ug', 'masiguro', 'nga', 'ang', 'natukod', 'nga', '8', 'ka', 'ektarya', 'nga', 'Eco-Park', ',', 'mo-operate', 'sa', 'iyang', 'kapasidad', 'ug', 'didto', 'madala', 'ang', 'basura', 'isip', 'garbage', 'sorter', 'sa', 'pasilidad', 'samtang', 'makamugna', 'og', 'kita', 'alang', 'sa', 'LGU', 'nga', 'labing', 'menos', 'P500,000', 'kada', 'buwan.', 'Kasamtangang', 'adunay', 'mga', 'makina', 'nga', 'gipadagan', 'sa', 'City', 'MRF', 'nga', 'naglakip', 'sa', 'pyrolysis', 'gasification', 'equipment', 'nga', 'mag-convert', 'sa', 'solid', 'waste', 'ngadto', 'sa', 'construction', 'materials', 'sama', 'sa', 'hollow', 'blocks', ',', 'bricks', 'ug', 'pavers', 'nga', 'gamiton', 'sa', 'mga', 'infrastructure', 'projects', ';', 'plastic', 'shredder', 'ug', 'densifiers', ';', 'bio-shredders', 'ug', 'composters', 'aron', 'ma-convert', 'ang', 'solid', 'waste', 'ngadto', 'sa', 'organic', 'fertilizer', ',', 'soil', 'conditioner', 'ug', 'mulch', 'aron', 'mapalambo', 'ang', 'pagtubo', 'sa', 'tanom', ';', 'glass', 'pulverizers', '/', 'shredder', 'ug', 'Plastic', 'Recycling', 'Equipment', 'nga', 'mahimong', 'lingkoranan', 'ang', 'mga', 'basura', 'naplastik.', 'Tungod', 'niini', 'ang', 'Dumaguete', 'City', 'gihatagan', 'og', 'award', 'isip', '“Best', 'in', 'Resource', 'Recovery', 'in', 'a', 'Centralized', 'Materials', 'Recovery', 'Facility”', 'sa', 'tanang', 'siyudad', 'ug', 'lungsod', 'sa', 'Rehiyon', '7', 'Central', 'Visayas', 'sa', 'Department', 'of', 'Environment', 'and', 'Natural', 'Resources-Environmental', 'Management', 'Bureau', 'VII', 'sa', 'miaging', 'tuig', '2022', '.'] 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
|
4,102
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KABISAY-AN', 'UG', 'UBAN', 'PANG', 'PARTE', 'SA', 'PH', ',', 'MAKASINATI', 'OG', 'PAG-ULAN', 'TUNGOD', 'SA', 'LPA', 'Makasinati', 'og', 'maulanon', 'nga', 'adlaw', 'ang', 'ubang', 'bahin', 'sa', 'nasud', 'tungod', 'sa', 'low', 'pressure', 'area', '(', 'LPA', ')', '.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'PAGASA', 'karong', 'adlawa', ',', 'May', '4', ',', '2023.', 'Sa', 'pinakabag-ong', 'public', 'weather', 'forecast', ',', 'katapusang', 'nakita', 'ang', 'LPA', 'sa', '130', 'km', 'sa', 'southeast', 'sa', 'Cuyo', ',', 'Palawan.', 'Nalakip', 'kini', 'sa', 'Intertropical', 'Convergence', 'Zone', '(', 'ITCZ', ')', 'nga', 'nakaapekto', 'sa', 'Southern', 'Luzon', ',', 'Visayas', ',', 'ug', 'Mindanao.', 'Makasinati', 'ang', 'Visayas', ',', 'Zamboanga', 'Peninsula', ',', 'Palawan', ',', 'Romblon', ',', 'Masbate', ',', 'Catanduanes', ',', 'Albay', ',', 'Sorsogon', ',', 'Dinagat', 'Islands', ',', 'Surigao', 'del', 'Norte', ',', 'ug', 'Surigao', 'del', 'Sur', 'og', 'madag-umon', 'nga', 'kalangitan', 'uban', 'sa', 'katag-katag', 'nga', 'pag-ulan', 'ug', 'pagdalugdog', 'tungod', 'sa', 'LPA', 'ug', 'ITCZ.', 'Dugang', 'pa', 'sa', 'PAGASA', ',', 'posible', 'ang', 'pagbaha', 'o', 'pagdahili', 'sa', 'yuta', 'tungod', 'sa', 'kasarangan', 'ug', 'usahay', 'kusog', 'nga', 'pag-ulan', 'sa', 'maong', 'mga', 'dapit', '.'] 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
|
4,103
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AIRASIA', ',', 'NAGTANYAG', 'OG', 'P55', 'NGA', 'PLITE', 'SUGOD', 'MAY', '1-7', 'Nagtanyag', 'ang', 'AirAsia', 'og', 'P55', 'nga', 'one-way', 'base', 'fare', 'gikan', 'niadtong', 'May', '1-7', ',', '2023.', 'Mahimong', 'mo-book', 'og', 'biyahe', 'gikan', 'sa', 'Manila', ',', 'Clark', ',', 'ug', 'Cebu', 'paingon', 'sa', 'mga', 'pili', 'nga', 'lokal', 'ug', 'internasyonal', 'nga', 'destinasyon.', 'Aduna', 'sab', 'sila'y', 'P955', 'nga', 'one-way', 'base', 'fare', 'paingon', 'sa', 'Tokyo', 'ug', 'Seoul', ',', 'Korea.', 'Aron', 'pag-upgrade', 'sa', 'travel', 'experience', ',', 'nagtanyag', 'sab', 'ang', 'AirAsia', 'og', '30', '%', 'sa', 'Hot', 'Seats.', 'Ang', 'maong', 'limited-time', 'sale', ',', 'aduna'y', 'travel', 'period', 'gikan', 'May', '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.
|
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|
cebuaner
|
4,104
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LPA', ',', 'GILAOMANG', 'MAGDALA', 'OG', 'PAG-ULAN', 'SA', 'KABISAY-AN', 'Usa', 'ka', 'bag-ong', 'Low', 'Pressure', 'Area', '(', 'LPA', ')', 'nga', 'anaa', 'sa', 'aktibong', 'Intertropical', 'Convergence', 'Zone', '(', 'ITCZ', ')', 'ang', 'naporma', 'sa', 'silangang', 'bahin', 'sa', 'Mindanao', 'sulod', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', '.', 'Sa', 'pagkakaron', ',', 'wala', 'pa', 'gisalikway', 'ang', 'posibilidad', 'nga', 'mahimo', 'kining', 'bagyo', 'sa', 'mga', 'mosunod', 'nga', 'adlaw', 'ug', 'gilaomang', 'moagi', 'sa', 'Visayas', 'ug', 'probinsya', 'sa', 'Palawan.', 'Kon', 'ugaling', 'mahimo', 'kining', 'bagyo', ',', 'pangalanan', 'kini', 'sa', 'PAGASA', 'og', '"', 'Betty.', '"', 'Gilaoman', 'sab', 'ang', 'pag-ulan', 'tungod', 'sa', 'naasoy', 'nga', 'LPA', 'sa', 'halos', 'tibuok', 'Visayas', ',', 'Mindanao', 'ug', 'bisan', 'paman', 'sa', 'Southern', 'Luzon', 'nga', 'posibleng', 'hinungdan', 'sa', 'kalit', 'nga', 'pagbaha', 'ug', 'pagdahili', 'sa', 'yuta', '.'] 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
|
4,105
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAGASA', ',', 'GI-ISYU', 'ANG', 'EL', 'NIÑO', 'ALERT', 'SA', 'NASUD', 'Gi-isyu', 'sa', 'Philippine', 'Atmospheric', ',', 'Geophysical', 'and', 'Astronomical', 'Services', 'Administration', '(', 'PAGASA', ')', 'ang', 'El', 'Niño', 'alert', 'karong', 'adlawa', ',', 'May', '2', ',', '2023.', 'Gibutyag', 'nila', 'nga', 'mahimong', 'motungha', 'sa', 'umalabot', 'nga', 'season', '(', 'June-July-August', ')', 'ang', 'weather', 'phenomenon', 'nga', 'aduna'y', 'below-normal', 'rainfall.', 'Samtang', 'aduna'y', 'negatibong', 'epekto', 'ang', 'El', 'Niño', 'sa', 'pipila', 'ka', 'dapit', 'sa', 'nasud', ',', 'mahimong', 'masinati', 'sa', 'kasadpang', 'bahin', 'sa', 'Pilipinas', 'ang', 'above-normal', 'rainfall', 'conditions', 'atol', 'sa', 'Southwest', 'Monsoon', 'season', '(', 'Habagat', ')', '.'] 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
|
4,106
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['WALA', 'KAYO', 'SA', 'LOLA', 'KO', '!', ''', '82-ANYOS', ',', 'NAG-SKYDIVE', 'SA', 'SIQUIJOR', 'Gipamatud-an', 'sa', 'usa', 'ka', '82-anyos', 'nga', 'lola', 'nga', 'wala', 'sa', 'edad', 'ang', 'adventure', '!', 'Bag-ohay', 'lang', ',', 'nisulay', 'pag-skydive', 'si', 'Iluminada', 'Fabroa', ',', 'usa', 'ka', 'kalampusan', 'nga', 'dili', 'mabuhat', 'sa', 'kadaghanan.', 'Ginganlan', 'sab', 'siya', 'isip', 'pinakagulang', 'nga', 'skydiver', 'sukad', 'sa', 'Skydive', 'sa', 'Siquijor.', 'Gibutyag', 'sa', 'mountaineer', 'nga', 'si', 'Jeremiah', 'Navarra', ',', 'apo', 'ni', 'Fabroa', ',', 'nga', 'kanunay', 'nga', 'nag-atang', 'sa', 'usa', 'ka', 'adventure', 'ang', 'iyang', 'lola.', 'Niadtong', 'December', '2022', ',', 'naabot', 'sab', 'ni', 'Fabroa', 'ang', 'kinatas-an', 'sa', 'Mt.', 'Apo.', 'Sumala', 'pa', 'niya', ',', 'daghan', 'pang', 'ganahan', 'nga', 'buhaton', 'ang', 'iyang', 'lola', 'nga', 'anaa', 'sa', 'ilang', 'bucket', 'list', 'lakip', 'na', 'ang', 'cliff', 'diving', 'ug', 'canyoneering.', 'Dugang', 'pa', 'ni', 'Navarra', ',', 'wala'y', 'gimentinar', 'nga', 'tambal', 'ang', 'iyang', 'lola', 'gawas', 'sa', 'multivitamins', 'ug', 'eye', 'drops', '.'] 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
|
4,107
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PINAY', 'NURSE', 'SA', 'AUSTRALIA', ',', 'NATAKTAK', 'HUMAN', 'GIINGONG', 'GIPASAGDAHAN', 'ANG', 'PASYENTE', 'ARON', 'MAKA-FACETIME', 'SA', 'PAMILYA', 'Gi-terminate', 'ang', 'pagparehistro', 'sa', 'usa', 'ka', 'kanhi', 'nars', 'sa', 'ospital', 'sa', 'Australia', 'human', 'napamatud-an', 'sa', 'local', 'nga', 'korte', 'nga', 'iyang', 'gitangtang', 'ang', 'heart', 'monitor', 'alarm', 'sa', 'usa', 'ka', 'tigulang', 'nga', 'pasyente', 'aron', 'makig-FaceTime', 'sa', 'iyang', 'pamilya', 'sa', 'Pilipinas.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'Australian', 'Broadcasting', 'Communication', ',', 'nadiskubrehang', 'patay', 'na', 'pagkahuman', 'ang', 'maong', 'pasyente', 'sa', 'Sydney', ''s', 'Nepean', 'Private', 'Hospital.', 'Nasayran', 'sa', 'New', 'South', 'Wales', 'Civil', 'and', 'Administrative', 'Appeals', 'Tribunal', 'nga', 'gipalong', 'ni', 'Geraldine', 'Lumbo', 'Dizon', 'ang', 'tingog', 'sa', 'vital', 'signs', 'monitor', 'atol', 'sa', 'iyang', 'night', 'shift', 'niadtong', 'July', '19', ',', '2021.', 'Human', 'sa', 'iyang', '66-minute', 'nga', 'tawag', 'sa', 'FaceTime', ',', 'nakalimot', 'ang', 'nars', 'pagpansak', 'og', 'balik', 'sa', 'alarm', 'system', 'human', 'sa', 'iyang', 'shift.', 'Matod', 'pa', 'sa', 'tribunal', ',', 'mao', 'kini', 'ang', 'hinungdan', 'sa', 'alarma', 'bahin', 'sa', 'nagkagrabe', 'nga', 'kondisyon', 'sa', 'pasyente', 'nga', 'wala', 'namatikdan', 'sa', 'mga', 'kawani.', 'Giingong', 'nagpabaya', 'ang', 'nars', 'sa', 'pagpahibalo', 'sa', 'mga', 'doktor', 'nga', 'aduna'y', 'irregular', 'heart', 'rhythm', 'ang', '85-anyos', 'nga', 'pasyente', 'usa', 'ka', 'oras', 'sa', 'wala', 'pa', 'kini', 'gideklarar', 'nga', 'patay.', 'Napakyas', 'sab', 'siya', 'sa', 'paghatag', 'og', 'tambal', 'sa', 'maong', 'pasyente', 'nga', 'aduna'y', 'heart', 'ug', 'renal', 'failure.', 'Nakita', 'sab', 'sa', 'CCTV', 'nga', 'kausa', 'ra', 'gisusi', 'sa', 'nars', 'ang', 'maong', 'pasyente', 'atol', 'sa', 'iyang', '10-hour', 'shift.', 'Gihukman', 'sa', 'korte', 'si', 'Dizon', 'kinsa', 'sad-an', 'sa', ''professional', 'misconduct', 'and', 'unsatisfactory', 'professional', 'conduct.', ''', 'Tungod', 'niini', ',', 'gisuspinde', 'og', 'usa', 'ka', 'tuig', 'ang', 'iyang', 'nurse', 'registration.', 'Sa', 'usa', 'ka', 'tribunal', 'hearing', ',', 'gibutyag', 'ni', 'Dizon', 'nga', 'iyang', 'gipalong', 'ang', 'speaker', 'aron', 'makatabang', 'sa', 'ubang', '"', 'confused', '"', 'nga', 'pasyente', 'kinsa', 'nagsige', 'og', 'bangon', 'sa', 'higdaanan', 'ug', 'nakaingon', 'siya', 'nga', 'ang', 'nag-alarm', 'mao', 'ang', 'doorbell.', 'Giangkon', 'sab', 'sa', 'nars', 'nga', 'nagtrabaho', 'siya', 'og', 'upat', 'ka', '10-hour', 'night', 'shifts', 'kada', 'semana', 'sa', 'Nepean', 'Public', 'Hospital', 'ug', 'tulo', 'ka', '10-hour', 'shifts', 'sa', 'Nepean', 'Private', '.'] 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 7, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 5, 6, 0]
|
cebuaner
|
4,108
|
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', ',', 'MOABOT', 'NA', 'SA', 'KAPIN', 'P158-M', 'Gibanabanang', 'moabot', 'ngadto', 'sa', 'P158', 'million', 'ang', 'jackpot', 'sa', 'Mega', 'Lotto', '6', '/', '45', 'sa', 'draw', 'karong', 'gabii', 'tungod', 'wala', 'pa'y', 'nakadaog', 'sa', 'maong', 'jackpot', 'sulod', 'sa', 'duha', 'ka', 'bulan.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Charity', 'Sweepstakes', 'Office', '(', 'PCSO', ')', 'kagahapong', 'adlawa', ',', 'Apr.', '30', ',', '2023.', 'Wala', 'pa'y', 'nakadaog', 'sa', 'draw', 'niini', 'niadtong', 'Biyernes', 'sa', 'gabii', ',', 'nga', 'aduna'y', 'winning', 'combination', 'nga', '27-15-04-42-38-45', 'ug', 'jackpot', 'nga', 'P146,353,791.20', '.'] 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, 7, 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, 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]
|
cebuaner
|
4,109
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PLDT', ',', 'GINGANLAN', 'ISIP', 'TOP', 'PH', 'INTERNET', 'SERVICE', 'PROVIDER', 'NIADTONG', '2022', 'Ginganlan', 'ang', 'PLDT', 'Inc.', 'isip', '"', 'fastest', 'fixed', 'internet', 'service', 'provider', '(', 'ISP', ')', '"', 'sa', 'nasud', 'sa', 'lima', 'ka', 'sunod-sunod', 'nga', 'tuig', 'sa', 'tinuig', 'nga', 'Ookla', 'Speedtest', 'Awards.', 'Gibutyag', 'sa', 'Ookla', 'nga', 'nakuha', 'sa', 'PLDT', 'ang', 'ilang', 'pinakataas', 'nga', 'speed', 'score', 'nga', 'anaa', 'sa', '86.52', ',', 'uban', 'sa', 'top', 'download', 'speeds', 'nga', '220.86', 'Megabits', 'per', 'second', '(', 'Mbps', ')', 'ug', 'top', 'upload', 'speeds', 'nga', '260.95', 'Mbps', '.'] 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, 3, 4, 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, 0, 3, 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]
|
cebuaner
|
4,110
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakita', 'sa', 'usa', 'ka', 'netizen', 'ang', 'duha', 'ka', 'iro', 'nga', 'naglutaw', 'ug', 'gigapos', 'ang', 'mga', 'tiil', 'sa', 'kadagatan', 'sa', 'lungsod', 'sa', 'Dalaguete', ',', 'sa', 'habagatang', 'bahin', 'sa', 'Cebu', 'niadtong', 'Domiggo', ',', 'Apr.', '30', ',', '2023.', 'Sumala', 'pa', 'sa', 'SunStar', 'Cebu', ',', 'niingon', 'sab', 'si', 'Melvan', 'Carter', 'nga', 'aduna', 'pa'y', 'higot', 'ang', 'usa', 'sa', '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, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,111
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PABAKUNAHAN', 'ANG', 'MGA', 'ANAK', 'LABAN', 'SA', 'POLIO', ',', 'RUBELLA', 'AT', 'TIGDAS', 'NGAYONG', 'MAY', '1-31', ',', '2023', 'Ipahigayon', 'sa', 'Department', 'of', 'Heath', '(', 'DOH', ')', 'ang', 'Chikiting', 'Ligtas', 'Campaign', 'karong', 'May', '1-31', ',', '2023.', 'Ipahigayon', 'ang', 'maong', 'kalihukan', 'aron', 'masiguro', 'nga', 'luwas', 'ang', 'mga', 'Chikiting', 'pinaagi', 'sa', 'pagpabakuna', 'batok', 'Polio', ',', 'Rubella', 'ug', 'Tigdas.', 'Mapanalipdan', 'ang', 'mga', 'Chikiting', 'nga', 'nag-edad', 'og', '0-59', 'ka', 'bulan', 'batok', 'polio', 'ug', 'mga', 'batang', 'nag-edad', 'og', '9-59', 'ka', 'bulan', 'batok', 'rubella', 'ug', 'tigdas.', 'Sa', 'Dumaguete', 'City', ',', 'matag', 'Lunes', 'hangtd', 'Biyernes', 'ang', 'schedule', 'sa', 'pagpabakuna.', 'Samtang', ',', 'aduna'y', 'ipahigayon', 'nga', 'supplemental', 'immunization', 'activity', 'matag', 'Lunes', 'ug', 'Biyernes', 'alang', 'sa', 'age', 'group', 'nga', '9', 'months', '-', '4', 'years', 'old', 'ug', '11', 'years', 'old'] 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, 3, 4, 4, 4, 4, 4, 0, 7, 8, 8, 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, 1, 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]
|
cebuaner
|
4,112
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nihakot', 'og', 'medalya', 'sa', 'CIVIRAA', '2023', 'ang', 'magsuong', 'estudyante', 'sa', 'Silliman', 'University.', 'Ang', 'magsuon', 'mao', 'sila', 'si', 'Kacie', 'Gabrielle', 'Marcelino', 'Tionko', ',', '15', 'anyos', ',', 'SUJHS', 'student', ',', 'ug', 'Isabelle', 'Jae', 'Tionko', ',', '11', 'anyos', ',', '6th', 'Grade', 'sa', 'SUES.', 'Nakuha', 'ni', 'Kacie', 'ang', '5', 'gold', 'medals', 'sa', 'Individual', 'Event', ',', '1', 'gold', 'medal', 'ug', '1', 'silver', 'medal', 'sa', 'team', 'relay.', 'Nadaog', 'ni', 'Isabelle', 'Jae', 'ang', '1', 'gold', 'medal', 'sa', '200m', 'Freestyle', 'ug', '2', 'gold', 'medals', 'sa', 'duha', 'ka', 'relay', 'events.', 'Samtang', ',', 'aduna', 'sab', 'siya'y', 'tag-usa', 'nga', 'bronze', 'medal', 'sa', '200m', 'IM', 'Bronze', 'ug', '100m', 'Backstroke', '.'] 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, 3, 4, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 3, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 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]
|
cebuaner
|
4,113
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BACOLOD', 'CHICKEN', 'INASAL', 'FESTIVAL', ',', 'MIBALIK', 'NA', 'HUMAN', 'SA', 'UPAT', 'KA', 'TUIG', 'Gianunsyo', 'sa', 'Bacolod', 'City', 'Government', 'ang', 'pagbalik', 'sa', 'tinuig', 'nga', 'kalihukan', 'nga', 'Bacolod', 'Chicken', 'Inasal', 'Festival', 'human', 'ang', 'upat', 'ka', 'tuig', 'nga', 'wala', 'kini', 'natigayon.', 'Ipahigayon', 'ang', 'maong', 'pista', 'sa', 'tulo', 'ka', 'managlahing', 'dapit', 'sa', 'Upper', 'East', ',', 'North', 'Capitol', 'Road', 'ug', 'Manokan', 'Country', 'karong', 'May', '26-28', ',', '2023.', 'Samtang', 'ipahigayon', 'ang', 'opening', 'ceremony', 'niini', 'sa', 'North', 'Capital', 'Road.', 'Lakip', 'sa', 'mga', 'kalihukan', 'nga', 'ipahigayon', 'sa', 'maong', 'kapistahan', 'ang', 'tasting', 'and', 'pairing', 'events', ',', 'grand', 'Inasal', 'tasting', 'pavilion', ',', 'Inasal', 'rewards', ',', 'Barangay', 'Inasal', 'cookoff', 'with', 'pre-event', 'workshop', ',', 'ug', 'longest', 'Inasal', 'grilling.', 'Dugang', 'pa', ',', 'ganahang', 'malabwan', 'sa', 'kapistahan', 'karong', 'tuiga', 'ang', '300', 'metros', 'nga', 'nakab-ot', 'sa', 'niaging', 'Bacolod', 'Chicken', 'Inasal', 'Festival', '.'] 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, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 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, 5, 6, 6, 6, 6, 6, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 8, 8, 0]
|
cebuaner
|
4,114
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tulo', 'ka', 'Division', 'sa', 'probinsya', 'ang', 'nalakip', 'sa', 'Top', '10', 'sa', '#', 'CVIRAA2023', 'final', 'medal', 'tally', 'karong', 'adlawa', ',', 'Apr.', '28', ',', '2023.', 'Anaa', 'sa', 'Top', '3', 'ang', 'Dumaguete', 'City', 'Division', 'nga', 'nakakuha', 'og', '38', 'gold', 'medals', ',', '31', 'silver', 'medals', 'ug', '44', 'bronze', 'medals.', 'Nakakuha', 'Bayawan', 'City', 'Division', 'og', '23', 'gold', 'medals', ',', '11', 'silver', 'medals', 'ug', '14', 'bronze', 'medals', 'diin', 'anaa', 'sila', 'sa', 'Top', '7.', 'Nabutang', 'sab', 'sa', 'Top', '9', 'ang', 'Negros', 'Oriental', 'Division', 'nga', 'nakakuha', 'og', '18', 'gold', 'medals', ',', '33', 'silver', 'medals', 'ug', '32', 'bronze', 'medals.', 'Samtang', ',', 'nakuha', 'na', 'sab', 'sa', 'Cebu', 'City', 'Division', 'ang', 'kampeyonato', 'human', 'makadaug', 'og', '110', 'gold', 'medals', ',', '93', 'silver', 'medals', ',', 'ug', '84', 'bronze', 'medals.', 'Gipahigayon', 'ang', 'CVIRAA', '2023', 'Meet', 'sa', 'Carcar', 'City', ',', 'Cebu', 'nga', 'nahuman', 'karong', '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, 0, 3, 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, 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, 3, 4, 4, 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, 7, 8, 8, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0]
|
cebuaner
|
4,115
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuha', 'ni', 'Janina', 'Alexandria', 'Anfone', 'Lintula', 'sa', 'Catherina', 'Cittadini', 'School', 'sa', 'Dumaguete', 'City', 'ang', '6', 'gold', 'medals', 'ug', '1', 'bronze', 'sa', 'pipila', 'ka', 'swimming', 'events', 'sa', 'CVIRAA', '2023.', 'Nadaog', 'niya', 'ang', 'gold', 'medals', 'sa', '50-meter', 'freestyle', ',', '100-meter', 'freestyle', ',', '50-meter', 'breaststroke', ',', '100-meter', 'breaststroke', ',', '4x50', 'medley', 'relay', 'ug', '4x50', 'relay', 'free', 'style.', 'Samtang', ',', 'nadaog', 'niya', 'ang', 'bronze', 'medal', 'sa', '50-meter', 'butterfly', 'event', '.'] 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, 1, 2, 2, 2, 0, 3, 4, 4, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,116
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuha', 'sa', 'estudyanteng', 'si', 'Sky', 'Gako', 'sa', 'Negros', 'Oriental', 'High', 'School', 'ang', 'duha', 'ka', 'gold', 'medals', 'alang', 'sa', '1500', 'meters', 'ug', '400', 'meters', 'freestyle', 'sa', 'unang', 'adlawa', 'sa', 'CVIRAA', '2023', 'Swimming', 'Competition', '.'] 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, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0]
|
cebuaner
|
4,117
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuha', 'sa', 'Dumaguete', 'City', 'Volleyball', 'Boys', 'and', 'Girls', '(', 'Elementary', ')', 'teams', 'ang', 'kampeyonato', 'sa', 'ilang', 'mga', 'kategorya', 'atol', 'sa', 'nagpadayon', 'nga', '#', 'CVIRAA2023', 'meet', 'sa', 'Carcar', 'City', ',', 'Cebu.', 'Nakuha', 'sa', 'mga', 'atleta', 'ang', 'gold', 'medal', 'alang', 'sa', 'ilang', 'mga', 'tagsa-tagsa', 'ka', 'kategorya', '.'] 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, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,118
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'ipahigayon', 'nga', 'Job', 'Fair', 'sa', 'Lamberto', 'Macias', 'Sports', 'and', 'Cultural', 'Complex', 'gikan', '8', ':', 'am', 'hangtod', '5:00pm', 'karong', 'Lunes', ',', 'May', '1', ',', '2023.', 'Giawhag', 'ang', 'mga', 'buot', 'motambong', 'sa', 'maong', 'job', 'fair', 'pagdala', 'sa', 'updated', 'resume', 'ug', 'uban', 'pang', 'importanteng', 'dokumento.', 'Giawhag', 'sab', 'kadtong', 'mga', 'overseas', 'applicants', 'nga', 'magdala', 'sa', 'kopya', 'sa', 'ilang', 'passports', '.'] 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,119
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giluwas', 'sa', 'upat', 'ka', 'bulan', 'nga', 'Belgian', 'Malinois', 'nga', 'itoy', 'ang', 'usa', 'ka', '5-anyos', 'nga', 'bata', 'sulod', 'sa', 'nasunog', 'nga', 'balay', 'sa', 'Barangay', 'Calumpang', 'sa', 'General', 'Santos', 'City', 'mga', 'alas', '2', 'sa', 'kadlawon', 'niadtong', 'Apr.', '20', ',', '2023.', 'Sumala', 'pa', 'sa', 'Facebook', 'post', 'ni', 'Jacquiline', 'Rufino', 'Madi', ',', 'kinsa', 'kasamtangang', 'nag-atimang', 'veterinarian', 'sa', 'itoy', 'nga', 'si', 'Princess.', 'Tungod', 'niini', ',', 'dali', 'nga', 'nidalagan', 'sa', 'gawas', 'ang', 'mga', 'miyembro', 'sa', 'pamilya', 'uban', 'ang', 'itoy.', 'Dali', 'sab', 'nga', 'gisunod', 'sa', 'amo', 'si', 'Princess', 'ug', 'nakita', 'nga', 'anaa', 'pa', 'sa', 'sulod', 'ang', 'iyang', 'anak', 'nga', 'nahinanok', 'pa', 'sa', 'pagkatulog.', 'Maayo', 'na', 'lang', ',', 'wala'y', 'namatay', 'sa', 'maong', 'insidente.', 'Gidala', 'si', 'Princess', 'sa', 'veterinary', 'clinic', 'niadtong', 'Apr.', '21', 'human', 'siya', 'nakaangkon', 'og', '3rd', 'degree', 'burn', 'sa', 'iyang', 'nawong', ',', 'mga', 'dalunggan', ',', 'ug', 'tiil.', 'Sa', 'pagkakaron', ',', 'anaa', 'si', 'Princess', 'sa', 'GSC', 'Pet', 'Care', 'Center', 'Veterinary', 'Hospital', 'human', 'ang', 'non-governmental', 'organization', 'nga', 'A', 'Heart', 'for', 'Paws', 'Gensan', 'nagbuhat', 'og', 'fundraising', 'drive', 'alang', 'niya', '.'] 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, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,120
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakadaug', 'og', '10', 'ka', 'gold', 'medal', 'ang', 'Bayawan', 'City', 'division', 'sa', 'wrestling', 'event', 'sa', '#', 'CVIRAA2023', 'meet', 'nga', 'gipahigayon', 'didto', 'sa', 'Carcar', 'City', ',', 'Cebu.', 'Tungod', 'niini', ',', 'nag-una', 'ang', 'Bayawan', 'City', 'partial', 'medal', 'tally', 'sa', 'maong', 'padula', 'sa', 'ulahing', 'datos', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'Cebu', 'Province', 'kagahapong', 'adlawa', ',', 'Apr.', '26', ',', '2023.', 'Nakuha', 'sab', 'sa', 'Bayawan', 'City', 'ang', '3', 'ka', 'silver', 'medal', 'ug', '2', 'ka', 'bronze', 'medal', 'sa', 'secondary', 'division', 'wrestling', 'competition', 'sa', 'naasoy', 'nga', 'sangka.', 'Ang', 'mga', 'nakadaug', 'og', 'gold', 'medals', 'alang', 'sa', 'Bayawan', 'City', 'mao', 'sila', 'si', 'Ruel', 'Jay', 'Rebutazo', ',', 'Ghavy', 'Anilov', 'Quianzo', ',', 'Klunt', 'Gladner', 'Macasling', ',', 'ug', 'Jejie', 'Dacula.', 'Nag-inusarang', 'gold', 'medalist', 'sa', 'cadet', 'secondary', 'boys', ''', 'category', 'si', 'Destine', 'Sean', 'Aliabo.', 'Sa', 'secondary', 'girls', ''', 'cadet', ',', 'nakuha', 'sab', 'sa', 'mga', 'mosunod', 'nga', 'atleta', 'ang', 'mga', 'bulawang', 'medalya', 'alang', 'sa', 'Bayawan', 'City', ':', 'Glia', 'Heart', 'Pahulayan', ',', 'Sweet', 'Vallery', 'Tumale', ',', 'ug', 'Kate', 'Leisly', 'Cabasag.', 'Gold', 'medalist', 'sab', 'sa', 'junior', 'girls', ''', 'category', 'sila', 'si', 'while', 'Khrystal', 'Myrrh', 'Claudel', 'ug', 'Crichel', 'Haydee', 'Michaela', 'Gaga-a', '.'] 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, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 3, 4, 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, 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, 3, 4, 0, 0, 0, 1, 2, 2, 0, 1, 2, 2, 0, 1, 2, 2, 0, 0, 1, 2, 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, 3, 4, 0, 1, 2, 2, 0, 1, 2, 2, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 2, 2, 0]
|
cebuaner
|
4,121
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'klase', 'sa', 'tanang', 'pampubliko', 'og', 'pribadong', 'elementary', 'ug', 'high', 'schools', 'sa', 'DUMAGUETE', 'CITY', 'karong', 'Huwebes', ',', 'Abril', '27', ',', '2023.', 'Kini', 'sigon', 'sa', 'usa', 'ka', 'direktiba', 'sa', 'DepEd', 'Dumaguete', 'City', 'Division', 'subay', 'sa', 'hangyo', 'ni', 'Mayor', 'Felipe', 'Remollo.', 'Matud', 'pa', 'sa', 'Dumaguete', 'City', 'PIO', ',', 'ang', 'pagsuspenso', 'sa', 'klase', 'usa', 'ka', 'paagi', 'aron', 'masiguro', 'nga', 'dili', 'maapektuhan', 'ang', 'mga', 'estudyante', 'sa', 'posibleng', 'traffic', 'atol', 'sa', 'panagtigom', 'sa', 'Free', 'and', 'Accepted', 'Masons', 'of', 'the', 'Philippines', 'ning', 'dakbayan.', 'Tungod', 'niini', ',', 'gimando', 'sa', 'DepEd', 'Dumaguete', 'City', 'ang', 'mga', 'eskuwelahan', 'ubos', 'niini', 'nga', 'magpatuman', 'og', 'distance', 'learning', 'alang', 'sa', 'mga', 'ilang', 'mga', 'tinun-an', '.'] 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, 3, 4, 4, 4, 0, 0, 0, 0, 0, 1, 2, 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, 3, 4, 4, 4, 4, 4, 4, 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]
|
cebuaner
|
4,122
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DICT', ':', 'MGA', 'DILI', 'REHISTRADONG', 'SIM', ',', 'POSIBLENG', 'DILI', 'NA', 'MAKA-FACEBOOK', 'UG', 'TIKTOK', 'Mahimong', 'makasinati', 'og', 'hinay-hinay', 'nga', 'pagkawala', 'sa', 'pribilehiyo', 'sa', 'mga', 'serbisyo', 'sa', 'SIM', 'kadtong', 'mapakyas', 'sa', 'pagparehistro', 'niini', 'sulod', 'sa', '90', 'ka', 'adlaw', 'nga', 'extension', 'period.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Department', 'of', 'Information', 'and', 'Communications', 'Technology', '(', 'DICT', ')', 'karong', 'adlawa', ',', 'Apr.', '25', ',', '2023.', 'Gisubli', 'ni', 'DICT', 'Secretary', 'Ivan', 'John', 'Uy', 'nga', 'tumong', 'sa', 'maong', '"', 'incentive', '"', 'nga', 'mapugos', 'ang', 'mga', 'tiggamit', 'sa', 'pagparehistro', 'sa', 'ilang', 'SIM', 'sa', 'wala', 'pa', 'ang', 'deadline.', 'Lakip', 'sa', 'mga', 'serbisyo', 'nga', 'mahimong', 'maapektaran', 'mao', 'ang', 'pag-access', 'sa', 'social', 'media', 'sites', 'sama', 'sa', 'Facebook', 'ug', 'Tiktok', ',', 'ingon', 'man', 'ang', 'outgoing', 'ug', 'incoming', 'nga', 'tawag', 'ug', 'text', '.'] 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, 7, 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,123
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PBBM', ':', 'PAGBALIK', 'SA', 'SUMMER', 'BREAK', 'SA', 'MARSO', ',', 'GITUN-AN', '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.', 'Apan', 'giklaro', 'ni', 'Marcos', 'nga', 'daghan', 'pa', 'kunong', 'kinahanglang', 'i-konsiderar', ',', 'lakip', 'na', 'ang', 'kahimtang', 'sa', 'nasud', 'sa', 'panahon', 'sa', 'pandemya', 'ingon', 'man', 'ang', 'dagan', 'sa', 'atong', 'klima.', 'Nipasalig', 'sab', 'siya', 'nga', 'mapagawas', 'na', 'sa', 'administrasyon', 'ang', 'desisyon', 'niini', '"', 'very', 'soon', '"', 'kon', 'sa', 'dili', 'madugay.', 'Daan', 'nang', 'gibarugan', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'nga', 'wala', 'kini', 'plano', '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.', 'Sa', 'wala', 'pa', 'ang', 'pandemya', ',', 'ang', 'klase', 'magsugod', 'sa', 'Hunyo', 'ug', 'matapos', 'sa', 'Marso.', 'Ang', 'pag-abli', 'sa', 'klase', 'sa', 'mga', 'tinun-an', 'sa', 'elementarya', 'ug', 'high', 'school', 'gibalhin', 'sa', 'Agosto', 'aron', 'maminusan', 'ang', 'kalisud', 'sa', 'mga', 'estudyante', 'sa', 'pag-adto', 'sa', 'klase', 'sa', 'panahon', 'sa', 'bagyo', '.'] 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.
|
[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, 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, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,124
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipalugwayan', 'na', 'og', 'dugang', '90', 'ka', 'adlaw', 'ang', 'panahon', 'sa', 'pagpa-register', 'sa', 'mga', 'SIM', 'card.', 'Mao', 'kini', 'ang', 'anunsyo', 'ni', 'Justice', 'Secretary', 'Jesus', 'Crispin', 'Remulla', 'karong', 'buntag', ',', 'Abril', '25', ',', '2023', ',', 'human', 'siya', 'mitambong', 'sa', 'usa', 'ka', 'multisectoral', 'meeting', 'nunot', 'sa', 'maong', 'isyu', 'nga', 'gipanguluhan', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'Ugma', ',', 'Abril', '26', ',', 'ang', 'orihinal', 'unta', 'nga', 'deadline', 'sa', 'SIM', 'card', '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, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,125
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAYOR', 'JANICE', 'DEGAMO', ',', ''EMOSYONAL', ''', 'HUMAN', 'MAKAATUBANG', 'SA', 'UNANG', 'HIGAYON', 'ANG', 'MGA', 'GIINGONG', 'NIPATAY', 'SA', 'BANA', 'Nakita', 'sa', 'unang', 'higayon', 'ni', 'Pamplona', 'Mayor', 'Janice', 'Degamo', 'niadtong', 'Lunes', 'ang', 'giingong', 'gunmen', 'nga', 'giingong', 'nipatay', 'sa', 'iyang', 'bana', 'nga', 'si', 'Gov.', 'Roel', 'Degamo.', 'Sumala', 'pa', 'sa', 'abogado', 'ni', 'Degamo', 'nga', 'si', 'Levito', 'Baligod', ',', 'nakig-atubang', 'ang', 'biyuda', 'sa', 'mga', 'suspek', 'sa', 'pagpatay', 'sa', 'iyang', 'bana', 'atol', 'sa', 'preliminary', 'investigation', 'sa', 'Department', 'of', 'Justice', '(', 'DOH', ')', 'sa', 'maong', 'krimen.', 'Human', 'sa', 'pagdungog', ',', 'gisubli', 'ni', 'Degamo', 'nga', 'nagkaduol', 'na', 'siya', 'sa', 'pagkab-ot', 'sa', 'hustisya', 'alang', 'sa', 'iyang', 'bana', 'ug', 'ubang', 'walo', 'ka', 'biktima', 'nga', 'napatay', 'niadtong', 'March', '4', '.'] 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, 2, 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, 1, 2, 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, 3, 4, 4, 4, 4, 4, 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]
|
cebuaner
|
4,126
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TAIWAN', ',', 'MAGTANYAG', 'OG', '500K', 'KA', 'DIGITAL', 'CARD', 'OG', 'HOTEL', 'VOUCHER', 'ALANG', 'SA', 'MGA', 'TURISTA', 'Magtanyag', 'ang', 'Taiwan', 'sa', 'mga', 'turista', 'og', 'digital', 'cards', 'nga', 'aduna'y', '$', '218', 'o', 'mga', 'voucher', 'sa', 'hotel', 'nga', 'pareha', 'og', 'kantidad.', '500,000', 'ka', 'mga', 'foreign', 'visitor', 'ang', 'makadawat', 'sa', 'vouchers', 'o', 'cards', 'pinaagi', 'sa', 'lucky', 'draw', 'system', 'pagsulod', 'nila', 'sa', 'Taiwan', 'sugod', 'sa', 'udto', 'sa', 'May', '1.', 'Kinahanglan', 'nga', 'magpa-pre-register', 'ang', 'mga', 'turista', 'alang', 'sa', 'draw', 'mga', 'usa', 'ka', 'adlaw', 'sa', 'wala', 'pa', 'sila', 'niabot', 'sa', 'Taoyuan', 'o', 'Songshan', 'airport', 'nga', 'nagserbisyo', 'sa', 'Taipei', ',', 'o', 'Taichung', 'and', 'Kaohsiung', 'international', 'airports', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 0, 5, 6, 0, 0, 0, 5, 0, 0, 5, 0, 5, 6, 6, 0]
|
cebuaner
|
4,127
|
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', 'hulagway', 'sa', 'driver', ''s', 'license', 'nga', 'giimprinta', 'sa', 'papel.', 'Temporaryo', 'nga', 'iisyu', 'ang', 'maong', 'papel', 'sa', 'mga', 'aplikante', 'samtang', 'naglisod', 'pa', 'ang', 'Land', 'Transportation', 'Office', '(', 'LTO', ')', 'sa', 'supply', 'sa', 'plastic', 'cards.', 'Sumala', 'pa', 'sa', 'LTO', ',', 'anaa', 'lang', 'sa', '147,000', 'ka', 'plastic', 'cards', 'ang', 'anaa', 'sa', 'tibuok', 'nasud.', 'Ug', 'mahimo', 'kining', 'hangtod', 'sa', 'katapusan', 'sa', 'Abril', '.'] 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, 3, 4, 4, 4, 4, 4, 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]
|
cebuaner
|
4,128
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakigkita', 'si', 'Presidente', 'Bongbong', 'Marcos', 'kang', 'Pamplona', 'Mayor', 'Janice', 'Degamo', 'ug', 'mga', 'uban', 'pang', 'biktima', 'sa', 'mga', 'insidente', 'sa', 'pagpamatay', 'sa', 'Negros', 'Oriental', 'didto', 'sa', 'Malacañang', 'Huwebes', 'sa', 'gabii', ',', 'Abril', '20', ',', '2023.', 'Mahinumduman', 'nga', 'sila', 'gikan', 'sa', 'serye', 'sa', 'mga', 'hearing', 'sa', 'Senado', 'kalabot', 'sa', 'pagpatay', 'kang', 'Gov.', 'Roel', 'Degamo', 'ug', 'uban', 'pang', 'insidente', 'sa', 'pagpamusil', '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, 1, 2, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,129
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAY', 'SA', 'CANDAU-AY', ',', 'GIPABUTHAN', 'OG', 'PUSIL', 'Nakuratan', 'ang', 'mga', 'residente', 'sa', 'Purok', 'Gumamela', 'sa', 'Barangay', 'Candau-ay', 'ning', 'dakbayan', 'human', 'gipabuthan', 'og', 'pusil', 'ang', 'usa', 'ka', 'balay', 'didto', 'Biyernes', 'sa', 'buntag', ',', 'Abril', '21', ',', '2023.', 'Matud', 'pa', 'sa', 'taho', 'sa', 'Dumaguete', 'PNP', ',', 'ang', 'maong', 'panimalay', 'gipanag-iya', 'sa', 'magtiayong', 'Gaspar', 'ug', 'Rhilrose', 'Antoniette', 'Torres', ',', 'pulos', '36', 'anyos.', 'Sigon', 'sa', 'inisyal', 'nga', 'imbestigasyon', 'sa', 'kapulisan', ',', 'natulog', 'kuno', 'ang', 'magtiayon', 'sa', 'dihang', 'gipukaw', 'sila', 'sa', 'ilang', 'katabang', 'pasado', 'alas-4', 'sa', 'kaadlawon', 'Biyernes', 'ug', 'gipahibal-an', 'sila', 'nga', 'dunay', 'namusil', 'sa', 'ilang', 'balay.', 'Gisusi', 'sa', 'magtiayon', 'sa', 'CCTV', 'footage', 'sa', 'ilang', 'panimalay.', 'Didto', 'nila', 'nasayran', 'nga', 'dunay', 'puti', 'nga', 'van', 'nga', 'niagi', 'sa', 'ilang', 'balay', 'ug', 'gipabuthan', 'kini', 'pasado', 'ala-1', 'sa', 'kaadlawon.', 'Gibutyag', 'sa', 'magtiayong', 'Torres', 'nga', 'naigo', 'sa', 'bala', 'ang', 'ilang', 'koral', 'nga', 'hinimo', 'sa', 'kawayan', 'ug', 'nilusot', 'kini', 'ngadto', 'sa', 'ilang', 'sakyanan.', 'Duha', 'ka', 'slug', 'sa', 'kalibre', '.45', 'nga', 'pistola', 'ang', 'na-recover', 'gikan', 'sa', 'crime', 'scene.', 'Padayon', 'ang', 'imbestigasyon', 'sa', 'Dumaguete', 'PNP', 'sa', 'maong', 'pagpamusil', '.'] 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, 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]
|
cebuaner
|
4,130
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makita', 'sa', 'video', 'ni', 'Mabinay', 'Mayor', 'Ernie', 'Jango', 'Uy', 'ang', 'paghagtok', 'sa', 'yelo', 'nga', 'nagtagaktak', 'sa', 'maong', 'lungsod', 'karong', 'buntag', ',', 'Abril', '21', ',', '2023.', 'Kini', 'taliwala', 'sa', 'init', 'nga', 'panahon', 'nga', 'padayong', 'nasinati', 'sa', 'ubang', 'bahin', 'sa', 'Negros', 'Oriental', 'dala', 'sa', 'dry', 'season', 'kon', 'ting-init', '.'] 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, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,131
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MABINAY', ',', 'GIPAULANAN', 'OG', 'YELO', 'Taliwala', 'sa', 'kainit', 'sa', 'panahon', 'nga', 'dala', 'sa', 'ting-init', ',', 'nakasinati', 'og', 'kusog', 'nga', 'hangin', 'ug', 'pag-ulan', 'og', 'yelo', 'ang', 'mga', 'residente', 'sa', 'lungsod', 'sa', 'Mabinay', 'karong', 'buntag', ',', 'Abril', '21', ',', '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.
|
[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, 5, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,132
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGOR', ',', 'NAGPATUMAN', 'NA', 'SAB', 'OG', 'BAN', 'SA', 'PAGPASULOD', 'OG', 'MGA', 'LANGGAM', 'PAGPAKGANG', 'SA', 'BIRD', 'FLU', 'Gipahamtang', 'sa', 'provincial', 'government', 'sa', 'Negros', 'Oriental', 'ang', 'total', 'ban', 'sa', 'tanan', '"', 'live', 'domestic', 'and', 'wild', 'birds', ',', 'ready', 'to', 'lay', 'pullets', 'and', 'manure', '"', 'gikan', 'sa', 'mga', 'lugar', 'nga', 'aduna'y', 'natahong', 'kaso', 'sa', 'Highly', 'Pathogenic', 'Avian', 'Influenza', '(', 'AI', ')', '.', 'Sumala', 'pa', 'kini', 'sa', 'gipagawas', 'nga', 'Executive', 'Order', '(', 'EO', ')', 'No.', '24', 'nga', 'ni', 'epekto', 'niadtong', 'Martes', ',', 'Apr.', '18', ',', '2023.', 'Ang', 'mga', 'apektadong', 'lugar', 'mao', 'ang', 'Luzon', ',', 'Mindanao', ',', 'Panay', ',', 'ug', 'Guimaras.', 'Aduna'y', 'mga', 'natahong', 'kaso', 'sa', 'AI', 'AH5N1', 'nga', 'nakaapekto', 'sa', 'mga', 'matang', 'sa', 'langgam', 'sama', 'sa', 'itik', ',', 'native', 'chickens', ',', 'commercial', 'chicken', 'layers', 'and', 'broilers', ',', 'gamefowls', ',', 'quails', ',', 'pigeons', ',', 'pet', 'birds', ',', 'ug', 'turkey.', 'Gipahamtang', 'kini', 'aron', 'masiguro', 'ang', 'pagpanalipod', 'sa', 'industriya', 'sa', 'manok', 'ug', 'sa', 'kinatibuk-ang', 'publiko', 'gikan', 'sa', 'dili', 'maayong', 'epekto', 'sa', 'sakit', 'nga', 'Bird', 'Flu', '.'] 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, 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, 7, 8, 8, 8, 8, 8, 8, 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, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,133
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['1ST', 'BATCH', 'SA', '2023', 'DUMAGUETE', 'FARM', 'TRAINEES', ',', 'MIABOT', 'NA', 'SA', 'SOUTH', 'KOREA', 'Miabot', 'na', 'sa', 'South', 'Korea', 'ang', 'unang', 'batch', 'sa', '32', 'ka', 'farm', 'trainees', 'gikan', 'sa', 'Dumaguete', 'City', 'niadtong', 'Sabado', ',', 'Apr.', '15', ',', '2023.', 'Personal', 'nga', 'nakigkita', 'nila', 'si', 'Mayor', 'Felipe', 'Remollo', 'sa', 'dili', 'pa', 'sila', 'molarga', 'paingon', 'sa', 'Mactan-Cebu', 'International', 'Airport', 'niadtong', 'Biyernes', 'sa', 'gabii.', 'Pag-abot', 'didto', ',', 'gi-welcome', 'sab', 'sila', 'ni', 'Yeongdong-gun', 'Mayor', 'Jeong', 'Yeong-Chul', 'uban', 'sa', 'ubang', 'opisyales.', 'Laing', 'batch', 'pa', 'sa', 'farm', 'trainees', 'ang', 'molarga', 'paingon', 'sa', 'South', 'Korea', 'sa', 'sunod', 'semana.', 'Ang', 'lungsod', 'sa', 'Yeongdong-gun', ',', 'naila', 'sa', 'pagprodyus', 'sa', 'mga', 'kalidad', 'nga', 'ubas', '.'] 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, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 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, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 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, 5, 6, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,134
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DICT', ':', 'SIM', 'REGISTRATION', ',', 'DILI', 'I-EXTEND', 'SA', 'PAGKAKARON', 'Dili', 'na', 'lugwayan', 'ang', 'April', '26', 'nga', 'deadline', 'sa', 'pagparehistro', 'sa', 'SIM', ',', 'nagpasabot', 'nga', 'ang', 'maong', 'petsa', 'mao', 'ang', 'kataposang', 'adlawa', 'sa', 'pagrehistro.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'Department', 'of', 'Information', 'and', 'Communications', 'Technology', '(', 'DICT', ')', 'karong', 'adlawa', ',', 'Apr.', '19', ',', '2023.', 'Sa', 'usa', 'ka', 'pahayag', ',', 'gibutyag', 'sa', 'DICT', 'nga', 'nadawat', 'nila', 'ang', 'hangyo', 'sa', 'mga', 'telcos', 'nga', 'lugwayan', 'ang', 'SIM', 'registration', 'nga', 'anaa', 'sa', 'SIM', 'Registration', 'Act.', 'Giawhag', 'sab', 'sa', 'DICT', 'ang', 'publiko', 'sa', 'pagparehistro', 'sa', 'SIM', 'tungod', 'usa', 'kini', 'ka', 'lakang', 'nga', 'gipanan-aw', 'nilang', 'makatabang', 'sa', 'pagwagtang', 'sa', 'mga', 'scam', 'nga', 'may', 'kalabotan', 'sa', 'SIM.', 'Dugang', 'pa', 'nila', ',', 'moresulta', 'sa', ''deactivation', ''', 'sa', 'SIM', 'kung', 'mapakyas', 'sa', 'pagparehistro', 'niini.', 'Tungod', 'niini', ',', 'mapunggan', 'ang', 'pagdawat', 'ug', 'pagpadala', 'og', 'tawag', 'ug', 'text', 'messages', 'ug', 'pag-access', 'sa', 'mga', 'importanteng', 'mobile', 'applications', '.'] 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 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, 7, 8, 8, 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, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,135
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGOR', ',', 'GI-BAN', 'ANG', 'PORK', 'PRODUCTS', 'GIKAN', 'SA', 'CEBU', 'UG', 'UBAN', 'PANG', 'LUGAR', 'NGA', 'APEKTADO', 'SA', 'ASF', 'Gipahamtang', 'sa', 'provincial', 'government', 'sa', 'Negros', 'Oriental', 'ang', 'total', 'ban', 'sa', 'baboy', 'ug', 'uban', 'pang', 'produkto', 'gikan', 'sa', 'probinsya', 'sa', 'Cebu', 'ug', 'uban', 'pang', 'mga', 'lugar', 'nga', 'apektado', 'sa', 'African', 'Swine', 'Fever', '(', 'ASF', ')', '.', 'Sumala', 'pa', 'kini', 'sa', 'gipagawas', 'nga', 'Executive', 'Order', '(', 'EO', ')', 'No.', '23', 'nga', 'ni-epekto', 'niadtong', 'Martes', ',', 'Apr.', '18', ',', '2023.', 'Gipahamtang', 'kini', 'aron', 'masiguro', 'ang', 'kaluwasan', 'sa', 'konstituwente', 'ug', 'lokal', 'nga', 'kahayupan', 'sa', 'probinsya', 'hangtod', 'nga', 'makontrolar', 'ang', 'ASF', 'nga', 'anaa', 'sa', 'lokal', 'nga', 'mga', 'baboy', 'sa', 'kasilinganang', 'mga', '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.
|
[5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 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, 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]
|
cebuaner
|
4,136
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'PATAY', 'HUMAN', 'GIPUSIL', 'SA', 'GIINGONG', 'MGA', 'MIYEMBRO', 'SA', 'NPA', 'SA', 'GUIHULNGAN', 'CITY', 'Patay', 'ang', 'duha', 'ka', 'lalaki', 'human', 'gipusil', 'sa', 'giingong', 'mga', 'miyembro', 'sa', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', 'sa', 'Sitio', 'Cambairan', ',', 'Barangay', 'Trinidad', 'sa', 'Guihulngan', 'City', 'mga', '11:50', 'sa', 'gabii', 'niadtong', 'Apr.', '18', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'sa', 'Guihulngan', 'ang', 'mga', 'biktima', 'nga', 'sila', 'si', 'Juven', 'Patagatay', 'Pasinabo', ',', '24', 'anyos', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'barangay', ',', 'ug', 'Jinny', 'Boy', 'Rapol', 'Tubias', ',', '29', 'anyos', ',', 'ug', 'residente', 'sa', 'Barangay', 'Montelya', ',', 'Moises', 'Padilla', 'sa', 'Negros', 'Occidental.', 'Sumala', 'pa', 'sa', 'imbestigasyon', 'sa', 'kapulisan', ',', 'nitambong', 'og', 'fiesta', 'si', 'Pasinabo.', 'Samtang', 'naglingkod', 'kini', 'daplin', 'sa', 'stage', 'aron', 'paglantaw', 'sa', 'disco', 'party', ',', 'kalit', 'lang', 'nga', 'niduol', 'ang', 'duha', 'ka', 'wala', 'pa', 'mailhing', 'suspek', 'ug', 'gipusil', 'siya', 'sa', 'makadaghang', 'higayon', 'samtang', 'nagsinggit', 'og', '"', 'NPA', 'MI', 'HAPA', 'ANG', 'WALAY', 'LABOT', '!', '"', 'Nakaangkon', 'og', 'samad', 'pinusilan', 'si', 'Pasinabo', 'sa', 'nagkalain-laing', 'parte', 'sa', 'iyang', 'kalawasan', 'hinungdan', 'sa', 'iyang', 'hinanaling', 'kamatayon.', 'Sa', 'laing', 'bahin', ',', 'nakasaksi', 'sa', 'maong', 'insidente', 'ang', 'laing', 'biktima', 'nga', 'si', 'Tubias.', 'Imbes', 'nga', 'mohapa', 'kini', 'ug', 'mangita', 'og', 'katagoan', ',', 'mihatag', 'kini', 'og', 'mando', 'sa', 'in-charge', 'sa', 'sound', 'system', 'nga', 'palungon', 'kini', 'ug', 'gisulay', 'pagklaro', 'kung', 'kinsa', 'ang', 'biktima', 'samtang', 'padayon', 'pang', 'gipusil', 'sa', 'mga', 'suspek', 'si', 'Pasinabo.', 'Human', 'niini', ',', 'gidala', 'sa', 'laing', 'duha', 'ka', 'mga', 'suspek', 'si', 'Tubias', 'sa', 'ngitngit', 'nga', 'dapit', 'ug', 'gipusil', 'sab', 'sa', 'makadaghang', 'higayon.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'aron', 'masikop', 'ang', 'mga', 'suspek', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 5, 6, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 2, 2, 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, 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, 0, 0, 0, 0, 1, 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, 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, 1, 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]
|
cebuaner
|
4,137
|
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', 'NALIGSAN', 'SA', 'TRUCK', 'DE', 'KARGA', 'Dead', 'on', 'the', 'spot', 'ang', 'usa', 'ka', 'motorista', 'human', 'kini', 'maligsan', 'sa', 'truck', 'de', 'karga', 'sa', 'National', 'Highway', 'Crossing', 'Rotonda', 'sa', 'Barangay', 'Poblacion', ',', 'Bayawan', 'City', 'mga', 'alas-7', 'sa', 'buntag', 'karong', 'adlawa', ',', 'Apr.', '18', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'sa', 'Bayawan', 'City', 'ang', 'drayber', 'sa', 'motorsiklo', 'nga', 'si', 'Binto', 'Cuenca', 'Gonzales', 'Jr.', ',', 'hingkod', 'ang', 'pangidaron', ',', 'ug', 'lumolupyo', 'sa', 'Sitio', 'Camandagan', ',', 'Barangay', 'Carnoche', 'sa', 'Santa', 'Catalina.', 'Samtang', ',', 'giila', 'ang', 'drayber', 'sa', 'maong', 'truck', 'de', 'karga', 'nga', 'si', 'Dindo', 'Cahigas.', 'Sumala', 'pa', 'sa', 'imbestigasyon', 'sa', 'kapulisan', ',', 'nagbiyahe', 'sa', 'samang', 'direksyon', 'ang', 'truck', 'ug', 'motorsiklo', 'paingon', 'sa', 'Brgy.', 'Poblacion.', 'Pag-abot', 'sa', 'crossing', 'sa', 'Rotonda', ',', 'ni-overtake', 'ang', 'motorista', 'sa', 'tuo', 'nga', 'bahin', 'sa', 'truck.', 'Giingong', 'aksidenteng', 'nasaghiran', 'sa', 'truck', 'ang', 'motorsiklo.', 'Tungod', 'niini', ',', 'natuwang', 'ang', 'motorsiklo', 'ug', 'naligiran', 'ang', 'ulo', 'sa', 'drayber', 'niini', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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]
|
cebuaner
|
4,138
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AIRASIA', ',', 'NAGTANYAG', 'OG', 'P88', 'NGA', 'PLITE', 'GIKAN', 'APRIL', '17-23', 'Nagtanyag', 'ang', 'AirAsia', 'Philippines', 'og', 'P88', 'nga', 'one-way', 'base', 'fare', 'gikan', 'Manila', ',', 'Cebu', ',', 'ug', 'Clark', 'paingon', 'sa', 'ubang', 'domestic', 'destinations', 'aron', 'tugutan', 'ang', 'dugang', 'nga', 'mga', 'Pilipino', 'nga', 'mobiyahe', 'atol', 'sa', 'summer', 'season.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'maong', 'airline', 'niadtong', 'Lunes', ',', 'Apr.', '18', ',', '2023.', 'Balido', 'ang', 'pag-book', 'sa', 'maong', 'promo', 'sa', 'Apr.', '17-23', 'nga', 'aduna'y', 'travel', 'period', 'gikan', 'Apr.', '17', 'hangtod', 'Oct.', '31', ',', '2023.', 'Tungod', 'sa', 'gilaomang', 'pagdasok', ',', 'giawhag', 'sa', 'AirAsia', 'ang', 'mga', 'pasahero', 'nga', 'moadto', 'sa', 'airport', 'mga', 'tulo', 'ka', 'oras', 'sa', 'dili', 'pa', 'molarga.', 'Mahimo', 'sab', 'nga', 'mo-check-in', 'ang', 'mga', 'bisita', 'pinaagi', 'sa', 'website', 'o', 'mobile', 'app', 'ingon', 'man', 'sa', 'self', 'check-in', 'kiosk', 'sa', 'airports', '.'] 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 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, 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]
|
cebuaner
|
4,139
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikalingawan', 'karon', 'sa', 'mga', 'netizen', 'ang', 'pagrampa', 'ni', '“Dolomite', 'Queen”', 'Renalyn', 'Macato', 'sa', 'iyang', 'pagbisita', 'ning', 'dakbayan', 'sa', 'Dumaguete.', 'Sa', 'iyang', 'post', 'bag-uhay', 'lamang', ',', 'makita', 'nga', 'nag-pose', 'si', 'Macato', 'samtang', 'nagsul-ob', 'og', 'bikini', 'sa', 'Pantawan', 'sa', 'Rizal', 'Boulevard.', 'Unang', 'nag-viral', 'sa', 'social', 'media', 'si', 'Macato', 'niadtong', 'tuig', '2020', 'sa', 'dihang', 'nagrampa', 'sab', 'kini', 'nga', 'naka-bikini', 'sa', 'Manila', 'Bay', 'Dolomite', 'Beach', 'nga', 'bag-o', 'lang', 'nag-abli', 'kaniadto', '.'] 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, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,140
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['REKISITOS', 'NGA', 'PERIODIC', 'MEDICAL', 'EXAM', 'ALANG', 'SA', 'MGA', 'DUNAY', 'LISENSYA', ',', 'GILIBKAS', 'NA', 'SA', 'LTO', 'Dili', 'na', 'obligahon', 'sa', 'Land', 'Transportation', 'Office', '(', 'LTO', ')', 'nga', 'moagi', 'og', 'dugang', 'nga', 'medical', 'examinations', 'ang', 'aduna'y', 'mga', 'driver', ''s', 'license', 'nga', 'balido', 'sa', 'lima', 'hangtod', 'pulo', 'ka', 'tuig.', 'Subay', 'kini', 'sa', 'direktiba', 'ni', 'LTO', 'Chief', 'Jay', 'Art', 'Tugade', 'nga', 'amyendahan', 'ang', 'LTO', 'Memorandum', 'Circular', '2021-2285', 'o', 'ang', 'Supplemental', 'Implementing', 'Rules', 'and', 'Regulations', 'of', 'Republic', 'Act', '10930.', 'Ubos', 'sa', 'maong', 'memorandum', ',', 'gawas', 'sa', 'regular', 'nga', 'medical', 'examination', 'isip', 'requirement', 'sa', 'aplikasyon', 'sa', 'bag-o', 'o', 'renewal', 'sa', 'driver', ''s', 'license', ',', 'obligado', 'sab', 'nga', 'mopailalom', 'sa', 'periodic', 'medical', 'exam', '(', 'PME', ')', 'ang', 'mga', 'nahatagan', 'og', '5-tuig', 'ug', '10-tuig', 'nga', 'validity', 'sa', 'lisensya', 'sa', 'pagmaneho.', 'Tungod', 'niini', ',', 'ubos', 'sa', 'gipatuman', 'nga', 'pag-amyenda', ',', 'kausa', 'nalang', 'ang', 'mandatory', 'medical', 'examination', 'ug', 'himuon', 'kini', 'sa', 'matag', 'higayon', 'sa', 'dili', 'pa', 'mokuha', 'o', 'mo-renew', 'sa', 'driver', ''s', 'license', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 3, 0, 1, 2, 2, 0, 0, 0, 7, 8, 8, 8, 0, 0, 7, 8, 8, 8, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,141
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Napalgang', 'patay', 'ang', 'usa', 'ka', 'lalaki', 'samtang', 'kini', 'naghulat', 'sa', 'iyang', 'biyahe', 'sulod', 'sa', 'pantalan', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'Lunes', 'sa', 'buntag', ',', 'April', '17', ',', '2023.', 'Giila', 'ang', 'nakalas', 'nga', 'si', 'Chrisler', 'Bernardo', 'Yunting', 'alyas', '"', 'Waslik', ',', '"', '46', 'anyos.', 'Lumad', 'nga', 'taga-Cotabato', 'City', 'si', 'Yunting', 'apan', 'kasamtangang', 'nagpuyo', 'sa', 'Barangay', 'Tambisan', ',', 'San', 'Juan', ',', 'Siquijor.', 'Sumala', 'pa', 'sa', 'taho', 'sa', 'pantalan', ',', 'natulog', 'lang', 'si', 'Yunting', 'samtang', 'naghulat', 'sa', 'iyang', 'biyahe', 'pauli', 'sa', 'Siquijor', 'Lunes', 'sa', 'kaadlawon.', 'Gisulay', 'siya', 'pagpukaw', 'sa', 'usa', 'ka', 'guwardiya', 'sa', 'pantalan', 'apan', 'wala', 'na', 'kuno', 'kini', 'nilihok', 'ug', 'nigahi', 'na', 'sab', 'ang', 'lawas', 'niini.', 'Sumala', 'pa', 'sa', 'mga', 'kauban', 'sa', 'trabaho', 'ni', 'Yunting', ',', 'nag-attend', 'pa', 'kuno', 'og', 'birthday', 'celebration', 'ang', 'biktima', 'sa', 'wala', 'pa', 'kini', 'mouli', 'unta', 'sa', 'Siquijor.', 'Wala', 'pa', 'mailhi', 'sa', 'pagkakaron', 'kung', 'unsay', 'hinungdan', 'sa', 'kamatayon', 'ni', 'Yunting.', 'Nagpadayon', 'pa', 'sab', 'karon', 'ang', 'post-mortem', 'examination', 'sa', 'mga', 'awtoridad', 'aron', 'matino', 'kung', 'nganong', 'namatay', 'ang', 'biktima', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 0, 1, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 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, 0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,142
|
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', 'PAGBALIK', 'SA', 'P1', 'NGA', 'PLITE', 'Nagtanyag', 'pagbalik', 'ang', 'Cebu', 'Pacific', 'sa', 'ilang', 'P1', 'nga', 'sale', 'aron', 'pagtugot', 'sa', 'mga', 'Pilipino', 'sa', 'pagplano', 'sa', 'ilang', 'gipangandoy', 'nga', 'bakasyon.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'maong', 'airline', 'karong', 'adlawa', ',', 'April', '17', ',', '2023.', 'Dugang', 'pa', 'nila', ',', 'aduna', 'kini', 'travel', 'period', 'gikan', 'August', '1', 'hangtod', 'March', '31', ',', '2024', '.'] 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, 3, 4, 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]
|
cebuaner
|
4,143
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tulo', 'ka', 'managsuon', 'ang', 'nakapasar', 'sa', '2022', 'Bar', 'Examinations.', 'Ang', 'managsuon', 'mao', 'sila', 'si', 'Manuel', 'Alexander', 'Orozco', 'Soriano', ',', 'Manuel', 'Joseph', 'Orozco', 'Soriano', 'ug', 'Manuel', 'Joshua', 'Orozco', 'Soriano.', 'Kagahapong', 'adlawa', ',', 'Apr.', '14', ',', 'gipagawas', 'sa', 'Supreme', 'Court', 'ang', 'resulta', 'sa', 'among', '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, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 2, 2, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,144
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuratan', 'ang', 'usa', 'ka', 'empleyado', 'sa', 'Shenzhen', ',', 'China', 'sa', 'dihang', 'nakadaug', 'siya', 'og', 'usa', 'ka', 'tuig', 'nga', 'paid', 'leave', 'atol', 'sa', 'ilang', 'annual', 'dinner', 'karong', 'semanaha.', 'Ang', 'mananaug', ',', 'usa', 'ka', 'lalaki', 'nga', 'gikatahong', 'manager', 'sa', 'iyang', 'kompanya.', 'Sumala', 'pa', 'sa', 'taho', 'sa', 'The', 'Straits', 'Times', ',', 'bisag', 'ang', 'empleyado', 'mismo', 'dili', 'katoo', 'nga', 'nakadaog', 'siya', 'sa', 'maong', 'dakong', 'premyo.', 'Gani', ',', 'gipangutana', 'pa', 'niya', 'sa', 'makadaghan', 'ang', 'iyang', 'mga', 'boss', 'kung', 'tinuod', 'ba', 'gyud', 'ang', 'iyang', 'nadag-an.', 'Hinuon', ',', 'ang', 'naasoy', 'nga', 'Chinese', 'company', 'nagkanayon', 'nga', 'padayon', 'pa', 'kining', 'nakigstorya', 'sa', 'empleyado', 'kung', 'ipa-encash', 'ba', 'niya', 'ang', 'iyang', 'leave', 'o', 'gamiton', 'niya', 'kini.', 'Gikatahong', '3', 'ka', 'tuig', 'nang', 'wala', 'gipahigayon', 'sa', 'maong', 'kompanya', 'ang', 'ilang', 'tinuig', 'nga', 'dinner', 'party', 'tungod', 'sa', '#', 'COVID19', 'pandemic.', 'Gihimo', 'kuno', 'kini', 'sa', 'kompanya', 'aron', 'mahatagan', 'ang', 'mga', 'empleyado', 'niini', 'og', 'kahupayan', 'gikan', 'sa', 'stress', 'sa', 'trabaho', '.'] 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, 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, 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, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,145
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'klase', 'ug', 'walay', 'trabaho', 'sunod', 'Biyernes', ',', 'Abril', '21', ',', '2023.', 'Kini', 'human', 'gideklarar', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'maong', 'adlaw', 'isip', 'regular', 'holiday', 'aron', 'pagsaulog', 'sa', 'Eid'l', 'Fitr', 'nga', 'maoy', 'katapusan', 'sa', 'panahon', 'sa', 'Ramadan', '.'] 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 7, 0]
|
cebuaner
|
4,146
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PSA', ':', 'SAKIT', 'SA', 'KASINGKASING', ',', 'NAG-UNANG', 'HINUNGDAN', 'SA', 'PAGKAMATAY', 'SA', 'NASUD', 'Ang', 'sakit', 'sa', 'kasingkasing', 'nga', 'Ischaemic', 'mao'y', 'nagpabiling', 'numero', 'uno', 'nga', 'hinungdan', 'sa', 'pagkamatay', 'sa', 'Pilipinas', 'gikan', 'January', 'hangtod', 'November', 'niadtong', '2022.', 'Base', 'kini', 'sa', 'bag-ong', 'datos', 'nga', 'gipagawas', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', '.', 'Sumala', 'pa', 'sa', 'PSA', ',', '103,628', 'ang', 'mga', 'namatay', 'tungod', 'sa', 'ischaemic', 'heart', 'diseases', 'o', '18.4', '%', 'sa', 'kinatibuk-ang', 'mga', 'nangamatay', 'sa', 'nasud', 'sulod', 'sa', 'samang', 'panahon.', 'Gipasabot', 'sab', 'sa', 'Philippine', 'Heart', 'Association', '(', 'PHA', ')', 'nga', 'mahitabo', 'ang', 'ischemic', 'heart', 'disease', 'o', 'coronary', 'artery', 'disease', 'kung', 'kulang', 'ang', 'oxygen', 'flow', 'sa', 'kasingkasing.', 'Tungod', 'kini', 'sa', 'pagpig-ot', 'sa', 'coronary', 'artery', 'nga', 'gikan', 'sa', 'pagtipon', 'sa', 'cholesterol', 'nga', 'magpalisod', 'sa', 'blood', 'flow', ',', 'ilabi', 'na', 'ang', 'oxygen.', 'Sa', 'samang', 'panahon', 'niadtong', '2021', ',', 'ang', 'top', '3', 'nga', 'hinungdan', 'sa', 'pagkamatay', 'mao', 'ang', 'ischaemic', 'heart', 'diseases', ',', 'cerebrovascular', 'diseases', ',', 'ug', 'COVID-19', '.'] 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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, 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, 7, 0]
|
cebuaner
|
4,147
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DICT', ':', ''GITUN-AN', ''', 'ANG', 'BASEHAN', 'SA', 'PAG-EXTEND', 'SA', 'DEADLINE', 'SA', 'SIM', 'REGISTRATION', 'Gitun-an', 'sa', 'Department', 'of', 'Information', 'and', 'Communications', 'Technology', '(', 'DICT', ')', ',', 'National', 'Telecommunications', 'Commission', '(', 'NTC', ')', 'ug', 'uban', 'pang', 'stakeholder', 'kung', 'aduna', 'ba'y', 'basehan', 'nga', 'lugwayan', 'ang', 'April', '26', 'nga', 'deadline', 'sa', 'pagparehistro', 'sa', 'SIM.', 'Mao', 'kini', 'ang', 'gibutyag', 'ni', 'DICT', 'Undersecretary', 'Jocelle', 'Batapa-Sigue', 'niadtong', 'Huwebes', ',', 'Apr.', '13', ',', '2023.', 'Niadtong', 'April', '11', ',', 'anaa', 'pa', 'lang', 'sa', 'kapin', '66', 'milyon', 'sa', 'kinatibuk-ang', '168', 'milyon', 'ka', 'mga', 'SIM', 'ang', 'narehistro.', 'Kung', 'mapakyas', 'sa', 'pagparehistro', ',', 'moresulta', 'kini', 'sa', 'pag-deactivate', 'sa', 'mobile', 'services.', 'Giawhag', 'sab', 'sa', 'Globe', 'Telecom', 'ug', 'Smart', 'Communications', 'ang', 'gobyerno', 'sa', 'pagpalugway', 'sa', 'SIM', 'registration', 'deadline', ',', 'diin', 'ilang', 'gisubli', 'ang', 'kakulang', 'sa', 'mga', 'valid', 'ID', 'hinungdan', 'sa', 'wala', 'pagparehistro', 'sa', 'uban', '.'] 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 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]
|
cebuaner
|
4,148
|
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', 'view', ''', 'sa', 'Central', 'Azucarera', 'de', 'Bais', 'pier', ',', 'diin', 'ipadala', 'ang', 'tubo', 'gikan', 'sa', 'pabrika', 'paingon', 'sa', 'barko', '(', 'SS', 'Legaspi', ')', 'pinaagi', 'sa', 'tren', 'sa', 'Barangay', 'Luca', 'sa', 'lungsod', 'sa', 'Tanjay', 'niadtong', '1939', '.'] 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, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0]
|
cebuaner
|
4,149
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagtigom', 'ang', 'mga', 'supporter', ',', 'kabanay', ',', 'ug', 'mga', 'kauban', 'sa', 'trabaho', 'ni', 'kanhing', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'karong', 'Miyerkoles', ',', 'Apr.', '12', ',', '2023', ',', 'aron', 'handumon', 'ang', 'ika-40', 'nga', 'adlaw', 'sukad', 'siya', 'gipatay', 'niadtong', 'Marso', '4.', 'Duha', 'ka', 'panagtigom', 'ang', 'gipahigayon', 'paghandum', 'sa', 'kanhing', 'gobernador', ':', 'usa', 'sa', 'iyang', 'lubnganan', 'sa', 'Bonawon', ',', 'Siaton', ',', 'ug', 'usa', 'sa', 'Negros', 'Oriental', 'Provincial', 'Capitol', '.'] 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, 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, 5, 6, 6, 0, 0, 0, 0, 3, 4, 4, 4, 0]
|
cebuaner
|
4,150
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakadawat', 'si', 'Rine', 'Christelle', 'G.', 'Anfone', 'og', 'P200,000', 'gift', 'incentive', 'human', 'siya', 'nalakip', 'sa', 'Top', '9', 'sa', '2022', 'Licensure', 'Examination', 'for', 'Teachers', '(', 'LET', ')', '.', 'Migradwar', 'si', 'Anfone', 'sa', 'Silliman', 'University', '(', 'SU', ')', 'diin', 'nakakuha', 'siya', 'kaniadto', 'og', '92.60', '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, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,151
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GIINGONG', 'SHABU', 'NGA', 'GIPUTOS', 'SA', 'CONDOM', ',', 'NASAKMIT', 'SA', 'NEGROS', 'ORIENTAL', 'PROVINCIAL', 'JAIL', 'Napugngan', 'sa', 'mga', 'awtoridad', 'ang', 'gikatahong', 'pagpayuhot', 'unta', 'og', '13', 'ka', 'pakete', 'sa', 'giingong', 'shabu', 'sulod', 'sa', 'Negros', 'Oriental', 'Detention', 'and', 'Rehabilitation', 'Center', '(', 'NODRC', ')', 'kagahapong', 'adlawa', ',', 'Abril', '11', ',', '2023.', 'Kini', 'human', 'nasakmit', 'sa', 'mga', 'sakop', 'sa', 'Philippine', 'Drug', 'Enforcement', 'Agency', '(', 'PDEA', ')', 'sa', 'Negros', 'Oriental', 'ang', 'maong', 'mga', 'pakete', 'nga', 'gisulod', 'sa', 'usa', 'ka', 'botelya', 'sa', 'deodorant', 'nga', 'giputos', 'sa', 'condom', 'didto', 'sa', 'naasoy', 'nga', 'prisohan.', 'Nasayran', 'sa', 'imbestigasyon', 'nga', 'unsa', 'ka', 'inmate', 'sa', 'NODRC', 'ang', 'giingong', 'hatdan', 'unta', 'sa', 'maong', 'mga', 'pakete', 'sa', 'shabu.', 'Ang', 'maong', 'inmate', ',', 'gikatahong', 'duna', 'nay', 'kaso', 'daan', 'nunot', 'sa', 'ilegal', 'nga', 'drogas.', 'Usa', 'ka', 'lalaki', 'ug', 'babaye', 'nga', 'nagsakay', 'og', 'motor', 'ang', 'nagbilin', 'sa', 'maong', 'mga', 'pakete', 'sa', 'shabu', 'sa', 'gawas', 'sa', 'NODRC', 'aron', 'itunol', 'kini', 'pasulod', 'sa', 'prisohan.', 'Nagbalor', 'og', 'P88,400', 'ang', 'nasakmit', 'nga', 'giingong', 'shabu', '.'] 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, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 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, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,152
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', ',', 'GIPUSIL', 'PATAY', 'SA', 'GIINGONG', 'NPA', 'SA', 'GUIHULNGAN', 'CITY', 'Patay', 'ang', 'usa', 'ka', 'lalaki', 'human', 'siya', 'gipusil', 'sa', 'Sitio', 'Mag-ubay', ',', 'Barangay', 'Mani-ak', 'sa', 'Guihulngan', 'City', 'mga', '7:50', 'sa', 'buntag', 'niadtong', 'Apr.', '11', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'sa', 'Guihulngan', 'ang', 'biktima', 'nga', 'si', 'Armando', 'Babor', ',', '33', 'anyos', ',', 'minyo', ',', 'usa', 'ka', 'panday', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sa', 'pakighinabi', 'sa', 'kapulisan', 'ngadto', 'sa', 'asawa', 'sa', 'biktima', ',', 'gibutyag', 'niya', 'nga', 'samtang', 'namahaw', 'sila', 'sa', 'ilang', 'pinuy-anan', 'kalit', 'lang', 'nga', 'nibutho', 'ang', 'tulo', 'ka', 'mga', 'armadong', 'lalaki', 'nga', 'nagsibilyan', 'og', 'sinina', 'ug', 'wala'y', 'duha-duha', 'nga', 'gipusil', 'ang', 'biktima.', 'Naigo', 'sa', 'ulo', 'ug', 'wala', 'nga', 'bukton', 'ang', 'biktima', 'nga', 'hinungdan', 'sa', 'iyang', 'hinanaling', 'kamatayon.', 'Gibutyag', 'sa', 'kapulisan', 'nga', 'tulo', 'ka', 'armadong', 'lalaki', 'ang', 'mga', 'suspek', 'kinsa', 'gituohang', 'mga', 'miyembro', 'sa', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', '.', 'Dali', 'sab', 'silang', 'niikyas', 'human', 'sa', 'insidente.', 'Nakuha', 'sa', 'crime', 'scene', 'ang', 'tulo', 'ka', 'basiyo', 'sa', 'bala', 'sa', 'cal.', '5.56mm.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'aron', 'mailhan', 'ang', 'mga', 'suspek', '.'] 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, 3, 0, 5, 6, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 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
|
4,153
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DALAI', 'LAMA', ',', 'NAG-SORRY', 'HUMAN', 'MIHANGYO', 'SA', 'BATA', 'PAGSUPSOP', 'SA', 'IYANG', 'DILA', 'Nangayo', 'og', 'pasaylo', 'ang', 'Tibetan', 'spiritual', 'leader', 'nga', 'si', 'Dalai', 'Lama', 'niadtong', 'Lunes', 'human', 'naka-trigger', 'og', 'backlash', 'sa', 'social', 'media', 'ang', 'usa', 'ka', 'video', 'nga', 'nagpakita', 'niya', 'nga', 'naghangyo', 'sa', 'usa', 'ka', 'batang', 'lalaki', 'sa', 'pagsupsop', 'sa', 'iyang', 'dila.', 'Nag-viral', 'ang', 'maong', 'video', 'diin', 'makita', 'si', 'Dalai', 'Lama', ',', '87', 'anyos', ',', 'nga', 'nihalok', 'sa', 'ngabil', 'sa', 'batang', 'lalaki', 'samtang', 'niyukbo', 'kini', 'aron', 'paghatag', 'og', 'respeto.', 'Makita', 'ang', 'maong', 'Buddhist', 'monk', 'nga', 'nagpagawas', 'sa', 'iyang', 'dila', 'samtang', 'naghangyo', 'sa', 'bata', 'sa', 'pagsupsop', 'niini.', '"', 'Can', 'you', 'suck', 'my', 'tongue', ',', '"', 'nadungog', 'niya', 'nga', 'pangutana', 'sa', 'bata', 'sa', 'video.', 'Nakuha', 'ang', 'maong', 'video', 'gikan', 'sa', 'usa', 'ka', 'panghitabo', 'sa', 'McLeod', 'Ganj', 'sa', 'dakbayan', 'sa', 'Dharamshala', 'sa', 'northern', 'India', 'niadtong', 'Feb.', '28', '.'] 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, 7, 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, 1, 2, 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, 5, 6, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0]
|
cebuaner
|
4,154
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAUDNON', 'PAG-IMBESTIGAR', 'SA', 'MGA', '‘BASTOS', 'UG', 'SUPLADO’', 'NGA', 'KAWANI', 'SA', 'GOBYERNO', ',', 'GIDUSO', 'NI', 'SEN.', 'TULFO', 'Gipasaka', 'ni', 'Sen.', 'Raffy', 'Tulfo', 'ang', 'usa', 'ka', 'resolusyon', 'nga', 'nagtinguhang', 'imbestigaran', 'ang', '"', 'arogante', 'ug', 'suplado', '"', 'nga', 'mga', 'trabahante', 'sa', 'gobyerno.', 'Gipangatarungan', 'ni', 'Sen.', 'Tulfo', 'nga', 'makamugna', 'ang', 'maong', 'kinaiya', 'og', '"', 'kahadlok', 'ug', 'pagkawala', 'sa', 'pagsalig', '"', 'ngadto', 'sa', 'mga', 'pampublikong', 'institusyon.', 'Makatabang', 'ang', 'imbestigasyon', 'sa', 'paghimo', 'sa', '"', 'Anti-Taray', 'Bill', '"', 'nga', 'mopahamtang', 'og', 'silot', 'ngadto', 'sa', 'mga', 'government', 'workers', 'nga', 'mapamatud-ang', 'sad-an', 'sa', '"', 'dili', 'maayong', 'pamatasan', ',', 'harassment', ',', 'ug', 'bisan', 'paman', 'human', 'rights', 'violations.', '"', 'Mahimo', 'silang', 'pahamtangan', 'og', 'silot', 'nga', '"', 'dismissal', 'from', 'service', '"', 'ug', '"', 'perpetual', 'disqualification', 'from', 'public', 'office.', '"', 'Sumala', 'pa', 'sa', 'Senador', ',', 'namugna', 'kini', 'nga', 'balaudnon', 'gikan', 'sa', 'mga', 'reklamo', 'nga', 'iyang', 'nakuha', 'gikan', 'sa', 'mga', 'Pilipino', 'nga', 'giingong', 'nakasulay', 'nga', '"', 'giinsulto', ',', 'gipakaulawan', ',', 'ug', 'gisingkahan', '"', 'sa', 'mga', 'trabahante', 'sa', 'gobyerno.', 'Gisubli', 'sab', 'ni', 'Tulfo', 'nga', 'kinahanglang', 'itrato', 'sa', 'mga', 'government', 'workers', 'ang', 'publiko', 'isip', '"', 'boss', '"', 'tungod', 'ang', 'ilang', 'kompensasyon', 'kay', 'gibayran', 'sa', 'mga', 'lungsuranon', '.'] 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, 1, 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, 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, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,155
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PNP', ':', '72', 'PATAY', 'HUMAN', 'MALUMOS', 'NIADTONG', 'SEMANA', 'SANTA', '72', 'ka', 'mga', 'tawo', 'ang', 'natala', 'nga', 'namatay', 'tungod', 'sa', 'mga', 'insidente', 'sa', 'pagkalumos', 'sa', 'tibuok', 'nasud', 'sukad', 'naidtong', 'April', '1.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'karong', 'adlawa', ',', 'Apr.', '10', ',', '2023.', 'Gisubli', 'ni', 'Fajardo', 'nga', 'aduna'y', 'multiple', 'deaths', 'sa', 'pipila', 'ka', 'mga', 'natala', 'nga', 'drowning', 'incidents', ',', 'sama', 'sa', 'mga', 'kaso', 'sa', 'gagmay', 'nga', 'bangka', 'o', 'sakayan', 'nga', 'matikyaob.', 'Natala', 'ang', 'kadaghanan', 'sa', 'mga', 'drowning', 'incidents', 'sa', 'Region', '1', '(', 'Ilocos', 'Region', ')', ',', 'Region', '3', '(', 'Central', 'Luzon', ')', ',', 'ug', 'Region', '4A', '(', 'CALABARZON', ')', '.', 'Dugang', 'pa', 'ni', 'Fajardo', ',', 'makigtinabangay', 'sila', 'sa', 'mga', 'lokal', 'nga', 'awtoridad', 'aron', 'pagpahinumdom', 'sa', 'mga', 'Pilipino', 'nga', 'dili', 'maglangoy', 'samtang', 'hubog', ',', 'ug', 'pagstorya', 'sa', 'mga', 'ginikanan', 'nga', 'bantayan', 'ang', 'ilang', 'mga', 'anak', 'samtang', 'naa', 'sa', 'tubig', '.'] 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, 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, 3, 4, 4, 4, 4, 4, 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, 6, 6, 0, 5, 6, 6, 6, 6, 6, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 1, 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]
|
cebuaner
|
4,156
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGSUON', 'SA', 'JIMALALUD', ',', 'PATAY', 'HUMAN', 'MATUMBAHAN', 'OG', 'LUBI', 'Patay', 'ang', 'duha', 'ka', 'managsuon', 'human', 'sila', 'matumbahan', 'sa', 'punoan', 'sa', 'lubi', 'samtang', 'nagsakay', 'og', 'motorsiklo', 'sa', 'National', 'Highway', ',', 'Barangay', 'South', 'Poblacion', 'sa', 'lungsod', 'sa', 'Jimalalud', 'mga', 'alas', '12', 'sa', 'udto', 'niadtong', 'Dominggo', ',', 'Apr.', '9', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'managsuon', 'nga', 'sila', 'si', 'Karl', 'Darwin', 'Magos', ',', '23', 'anyos', ',', 'ug', 'Clyde', 'Magos', ',', '31', 'anyos', ',', 'kinsa', 'puros', 'ulitawo', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'barangay.', 'Nasayran', 'sa', 'report', 'nga', 'nagbiyahe', 'ang', 'managsuon', 'sa', 'national', 'highway', 'samtang', 'sakay', 'sa', 'motorsiklo', 'sa', 'dihang', 'kalit', 'lang', 'nga', 'natumba', 'ang', 'punoan', 'sa', 'lubi', 'ug', 'didto', 'nihagsa', 'nila.', 'Nagbiyahe', 'unta', 'sila', 'paingon', 'sa', 'dagat', 'sa', 'Barangay', 'Bae', 'aron', 'mangaligo.', 'Sumala', 'pa', 'ni', 'Assistant', 'Investigator', 'nga', 'si', 'SSg', 'Joey', 'Patoc', ',', 'nga', 'patay', 'diha-diha', 'dayon', 'ang', 'managsuon', 'tungod', 'sa', 'nahitabo.', 'Dugang', 'pa', 'niya', ',', 'natumba', 'ang', 'punoan', 'tungod', 'gamay', 'nalang', 'ang', 'naghawid', '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, 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, 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, 1, 2, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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]
|
cebuaner
|
4,157
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikatahong', 'nagbulag', 'na', 'ang', 'inilang', 'singer-songwriter', 'nga', 'si', 'Taylor', 'Swift', 'ug', 'ang', 'aktor', 'nga', 'uyab', 'niini', 'nga', 'si', 'Joe', 'Alwyn.', 'Sumala', 'pa', 'sa', 'taho', 'sa', 'Entertainment', 'Tonight', ',', 'pipila', 'na', 'ka', 'ang', 'semana', 'ang', 'milabay', 'sukad', 'nga', 'gitapos', 'sa', 'pares', 'ang', 'ilang', '6', 'ka', 'tuig', 'nga', 'relasyon.', 'Kini', 'ang', 'giingong', 'hinungdan', 'nganong', 'wala', 'nagtambong', 'si', 'Alwyn', 'sa', 'mga', 'ulahing', 'concert', 'ni', 'Swift', '.'] 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 1, 0, 0, 0, 0, 0, 1, 0]
|
cebuaner
|
4,158
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['VIA', 'CRUCIS', 'SA', 'DUMAGUETE', 'LOOK', ':', 'Gatosan', 'ka', 'Dumagueteño', 'ang', 'nitambong', 'sa', 'Via', 'Crucis', 'kon', 'Way', 'of', 'the', 'Cross', 'karong', 'Biyernes', 'Santo', ',', 'Abril', '7', ',', '2023', ',', 'aron', 'handumon', 'ang', 'mga', 'katapusang', 'panghitabo', 'sa', 'kinabuhi', 'ni', 'Jesu-Kristo', 'lakip', 'na', 'ang', 'paglansang', 'Kaniya', 'sa', 'krus', '.'] 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, 5, 0, 0, 0, 0, 7, 0, 0, 0, 7, 8, 0, 7, 8, 8, 8, 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]
|
cebuaner
|
4,159
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PBBM', ',', 'NAGTUKOD', 'NA', 'OG', 'TASK', 'FORCE', 'ARON', 'MASIGURO', 'ANG', 'KALINAW', 'SA', 'NEGROS', 'ORIENTAL', 'HUMAN', 'SA', 'PAGPATAY', 'KANG', 'GOV.', 'DEGAMO', 'Giaprobahan', 'ni', 'President', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'paghimo', 'og', 'joint', 'ug', 'special', 'task', 'force', 'human', 'sa', 'pagpatay', 'ni', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo.', 'Ubos', 'sa', 'Administrative', 'Order', 'No.', '6', ',', 'nga', 'gipirmahan', 'niadtong', 'Apr.', '3', ',', 'gitumong', 'ang', '"', 'Special', 'Task', 'Force', 'Degamo', '"', 'aron', '"', 'prevent', 'the', 'spread', 'and', 'escalation', 'of', 'violence', 'elsewhere', 'in', 'the', 'Philippines', 'and', 'to', 'maintain', 'peace', 'and', 'order', 'in', 'Negros', 'Island', ',', 'with', 'due', 'regard', 'to', 'the', 'fundamental', 'civil', 'and', 'political', 'rights', 'of', 'the', 'people.', '"', 'Sumala', 'pa', 'sa', 'pahayag', 'sa', 'Presidential', 'Communications', 'Office', ',', 'pangunahan', 'ni', 'Interior', 'Secretary', 'Benhur', 'Abalos', 'ang', 'maong', 'task', 'force', 'uban', 'ni', 'Justice', 'Secretary', 'Jesus', 'Crispin', 'Remulla', 'isip', 'co-chairperson.', 'Nipasalig', 'sab', 'ang', 'AFP', 'sa', 'niaging', 'bulan', 'nga', 'walay', 'militarisasyon', 'sa', 'isla', 'human', 'nagpadala', 'og', 'dugang', 'mga', 'sundalo', 'nganhi', 'sa', 'Negros', 'aron', 'masiguro', 'ang', 'kalinaw', 'dinhi', '.'] 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.
|
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 5, 6, 6, 6, 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, 5, 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, 3, 4, 4, 0, 0, 0, 0, 0, 1, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,160
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['14-ANYOS', 'NGA', 'DALAGA', ',', 'GITIGBAS', 'PATAY', 'SA', 'GUIHULNGAN', 'CITY', 'Nakaplagan', 'ang', 'patay', 'nga', 'lawas', 'sa', 'usa', 'ka', '14-anyos', 'nga', 'babayi', 'sa', 'Sitio', 'Anislag', ',', 'Barangay', 'T-Hill', 'sa', 'Guihulngan', 'City', 'kagabii', ',', 'April', '4', ',', '2023.', 'Giila', 'sa', 'Guihulngan', 'Police', 'Station', 'ang', 'biktima', 'nga', 'usa', 'ka', 'Grade', '8', 'nga', 'estudyante', 'sa', 'Magsaysay', 'National', 'High', 'School', ',', 'kinsa', 'lumolupyo', 'sab', 'sa', 'naasoy', 'nga', 'barangay.', 'Sumala', 'pa', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'aduna'y', 'samad', 'tinigbasan', 'sa', 'liog', 'ug', 'uban', 'pang', 'parte', 'sa', 'kalawasan', 'ang', 'biktima.', 'Dugang', 'pa', 'sa', 'kapulisan', ',', 'nakita', 'pa', 'sa', 'mga', 'silingan', 'ang', 'biktima', 'nga', 'padulong', 'sa', 'eskwelahan', 'anang', 'buntag', 'apan', 'wala', 'na', 'kini', 'nakaabot', 'sa', 'iyang', 'klase.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'aron', 'masikop', 'ang', 'suspek', '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, 5, 6, 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, 3, 4, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,161
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mahitabo', 'ang', 'labing', 'talagsaon', 'nga', 'solar', 'eclipse', 'karong', 'April', '20', ',', '2023', ',', 'nga', 'gitawag', 'nga', 'hybrid', 'solar', 'eclipse.', 'Mahimong', 'makita', 'sa', 'mga', 'residente', 'sa', 'Dumaguete', 'City', ',', 'Bacong', ',', 'Dauin', ',', 'Zamboanguita', 'ug', 'Siaton', 'ang', 'halos', '50', '%', 'sa', 'adlaw', 'nga', 'matabunan', 'sa', 'anino', 'sa', 'bulan', 'mga', '12:50', 'sa', 'udto.', 'Mahitabo', 'ang', 'hybrid', 'solar', 'eclipse', 'halos', 'kausa', 'lang', 'sa', 'matag', 'dekada', '.'] 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, 5, 6, 0, 5, 0, 5, 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, 0, 0, 0, 0]
|
cebuaner
|
4,162
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ginganlan', 'sa', 'NASA', 'ang', 'upat', 'ka', 'mga', 'astronauts', 'nga', 'moadto', 'sa', 'bulan', 'atol', 'sa', 'misyon', 'sa', 'Artemis', 'II.', 'Ang', 'mga', 'astronaut', 'mao', 'sila', 'si', 'Reid', 'Wiseman', ',', 'Victor', 'Glover', ',', 'Jeremy', 'Hanson', 'ug', 'Christina', 'Koch.', 'Si', 'Koch', 'ang', 'unang', 'babayi', 'nga', 'ma-assign', 'sa', 'usa', 'ka', 'lunar', 'mission', ',', 'samtang', 'si', 'Glover', 'mao', 'ang', 'unang', 'Black', 'astronaut', '.'] 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, 7, 8, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,163
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Maagian', 'na', 'sa', 'mga', 'motorista', 'ang', 'bahin', 'sa', 'Camanjac-Magatas', 'Metro', 'Dumaguete', 'Diversion', 'Road', 'human', 'natangtang', 'na', 'ang', 'alad', 'ug', 'nasemento', 'na', 'ang', 'dalan', 'niini.', 'Mahimo', 'sab', 'nga', 'makaagi', 'ang', 'mga', 'dagkong', 'trak', 'dekarga', 'ug', 'mga', 'pribadong', 'sakyanan', 'gikan', 'sa', 'Barangay', 'Camanjac', 'lapos', 'sa', 'Barangay', 'Candau-ay', 'luyo', 'sa', 'Vida', 'Royal', 'padulong', 'Cadawinonan', ',', 'Junob', ',', 'Cantil-e', ',', 'Bajumpandan', 'ug', 'Bacong-Valencia', 'Road', 'sa', 'Isugan', 'Bacong', '.'] 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, 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, 5, 6, 0, 0, 5, 6, 0, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 6, 0]
|
cebuaner
|
4,164
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'Negrense', 'nagtapok', 'sa', 'Mabinay', 'Spring', 'karong', 'adlawa', ',', 'Abril', '2', ',', '2023', 'aron', 'magpabugnaw', 'gikan', 'sa', 'hilabihang', 'kainit', 'sa', 'panahon', 'atol', 'sa', 'Dominggo', 'sa', 'Lukay', '.'] 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, 7, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0]
|
cebuaner
|
4,165
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'KAWANI', 'SA', 'GOBYERNO', ',', 'WALA', 'NAY', 'TRABAHO', 'SUGOD', 'SA', 'HAPON', 'SA', 'ABRIL', '5', 'Gisuspende', 'ang', 'pagtrabaho', 'sa', 'mga', 'opisina', 'sa', 'gobyerno', 'karong', 'April', '5', ',', '2023', 'alang', 'sa', 'Semana', 'Santa.', 'Sa', 'gipagawas', 'nga', 'Memorandum', 'Circular', 'No.', '16', ',', 'gisuspende', 'ang', 'maong', 'adlaw', 'aron', 'hingpit', 'nga', 'mahatagan', 'og', 'higayon', 'ang', 'tanang', 'government', 'employees', 'sa', 'pag-obserbar', 'sa', 'Semana', 'Santa', '.'] 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, 7, 8, 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, 7, 8, 0]
|
cebuaner
|
4,166
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['247', 'ka', 'mga', 'CCTV', 'ang', 'gikomisyon', 'ug', 'gi-activate', 'sa', 'Bais', 'City', 'sa', 'kompanya', 'nga', 'Mega', 'Speed', '.'] 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, 3, 4, 0]
|
cebuaner
|
4,167
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mas', 'gipasayon', 'sa', 'GCash', 'ang', 'international', 'travels', 'pinaagi', 'sa', 'bag-ong', 'GCash', 'card', 'ug', 'bag-ong', 'Visa', 'card', 'nga', 'magamit', 'sa', '100', 'million', 'ka', 'mga', 'tindahan', 'sa', 'kapin', '200', 'ka', 'mga', '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, 7, 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]
|
cebuaner
|
4,168
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nadiskubre', 'sa', 'mga', 'astronomers', 'ang', 'usa', 'sa', 'pinakadako', 'nga', 'black', 'holes', 'gamit', 'ang', 'usa', 'ka', 'bag-ong', 'teknik', 'nga', 'makakita', 'sa', 'liboan', 'pa', 'ka', 'mga', 'celestial', 'monsters', 'sa', 'mga', 'mosunod', 'nga', 'tuig.', 'Ang', 'maong', 'hulagway', 'nga', 'gipagawas', 'sa', 'ESA', 'Hubble', 'niadtong', '2022', 'nagpakita', 'sa', 'impresyon', 'sa', 'usa', 'ka', 'artist', 'sa', 'black', 'hole', 'sa', 'Milky', 'Way', 'galaxy', ',', 'diin', 'gina-', '"', 'distort', '"', 'niini', 'ang', 'kahayag', 'nga', 'moagi', 'duol', 'niini', 'nga', 'mahimong', 'phenomenon', 'o', 'mas', 'giila', 'nga', '"', 'gravitational', 'lensing', '.', '"'] 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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]
|
cebuaner
|
4,169
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPIN', 'P34-M', 'NGA', 'JACKPOT', 'SA', 'GRAND', 'LOTTO', '6', '/', '55', ',', 'SOLONG', 'NADAG-AN', 'Usa', 'ka', 'tigpatad', 'ang', 'nakadaog', 'sa', 'kapin', 'P34', 'milyon', 'nga', 'jackpot', 'prize', 'sa', 'Philippine', 'Charity', 'Sweepstakes', 'Office', '(', 'PCSO', ')', 'niadtong', 'Miyerkules', ',', 'Mar.', '29', ',', '2023.', 'Ang', 'winning', 'combination', 'sa', 'maong', 'dula', 'mao', 'ang', '26-23-34-41-45-29', 'alang', 'sa', 'premyo', 'nga', 'P34,123,859.', 'Wala', 'pa', 'sab', 'nakakuha', 'sa', 'kombinasyon', 'nga', '32-23-31-34-40-29', 'sa', 'Mega', 'Lotto', '6', '/', '45', 'nga', 'aduna'y', 'premyo', 'nga', 'dul-an', 'P48', 'milyon', '.'] 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, 8, 8, 8, 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, 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, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,170
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['P2.1-M', 'NGA', 'BALOR', 'SA', 'ILEGAL', 'NGA', 'DRUGAS', ',', 'NASAKMIT', 'SA', 'KAPULISAN', 'SA', 'UNANG', '3', 'KA', 'BULAN', 'SA', '2023', 'Gibutyag', 'sa', 'Dumaguete', 'City', 'Police', 'Station', 'nga', 'nasakmit', 'nila', 'ang', 'kinatibuk-ang', '316.32', 'grams', 'sa', 'ilegal', 'nga', 'drugas', 'nga', 'mobalor', 'og', 'P2.1', 'million', 'sa', 'unang', 'tulo', 'ka', 'bulan', 'sa', 'tuig', '2023.', 'Gikan', 'kini', 'sa', '10', 'ka', 'operasyon', 'nga', 'gipahigayon', 'sa', 'kapulisan', 'uban', 'sa', 'pagkasikop', 'sa', '10', 'ka', 'mga', 'suspek.', 'Gipresentar', 'ang', 'maong', 'mga', 'report', 'atol', 'sa', '1st', 'quarter', 'meeting', 'sa', 'City', 'Peace', 'and', 'Order', 'Council', 'niadtong', 'Biyernes', ',', 'Mar.', '24', ',', '2023.', 'Sa', 'laing', 'bahin', ',', '64', 'ka', 'mga', 'wanted', 'person', 'ang', 'nasikop', 'ug', '9', 'ka', 'loose', 'firearms', 'ang', 'nakumpiska', 'gikan', 'sa', 'nagkalain-laing', 'operasyon', 'sa', 'kapulisan', 'sa', 'samang', 'panahon.', 'Kung', 'itandi', 'sa', '1st', 'quarter', 'sa', '2022', ',', 'nikunhod', 'og', '2.48', '%', 'ang', 'crime', 'incidents', 'sa', 'unang', 'tulo', 'sa', 'bulan', 'sa', '2023.', 'Sa', 'niaging', 'tuig', 'aduna'y', '121', 'nga', 'natalang', 'crime', 'incidents', ',', 'samtang', 'aduna'y', '118', 'karong', 'tuiga', '.'] 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,171
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PAG-IBIG', ',', 'GIANUNSYO', 'ANG', 'DIVIDEND', 'RATES', 'SA', 'REGULAR', 'SAVINGS', 'UG', 'MP2', 'SAVINGS', 'Gianunsyo', 'sa', 'Pag-IBIG', 'Fund', 'ang', 'gipaabot', 'nga', 'Pag-IBIG', 'Savings', 'Dividend', 'rates', 'alang', 'sa', '2022', 'atol', 'sa', 'Chairman', ''s', 'Report', 'niini', 'niadtong', 'Mar.', '28', ',', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 7, 8, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,172
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ASH', 'KETCHUM', 'UG', 'PIKACHU', ',', 'NANAMILIT', 'NA', 'ISIP', 'MGA', 'BIDA', 'SA', 'POKEMON', 'Opisyal', 'na', 'nga', 'natapos', 'ang', 'minahal', 'nga', 'mga', 'karakter', 'nga', 'sila', 'si', 'Ash', 'Ketchum', 'ug', 'Pikachu', 'isip', 'lead', 'stars', 'sa', '"', 'Pokemon', '"', 'series.', 'Kini', 'human', 'giila', 'si', 'Ketchum', 'isip', 'Pokemon', 'world', 'champion', 'sa', 'niaging', 'tuig', 'human', 'sa', '25-year-stint', 'sa', 'maong', 'salida.', 'Sa', 'ilang', 'social', 'media', 'accounts', ',', 'gi-post', 'sa', 'Pokemon', 'ang', 'isa', 'ka', 'clip', 'ni', 'Ketchum', 'ug', 'Pikachu', 'nga', 'nibiya', 'sa', 'lungsod', 'nga', 'nagpasabot', 'sa', 'katapusan', 'sa', 'ilang', ''journey', '.', '''] 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.
|
[1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 1, 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, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,173
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['314', 'DENGUE', 'CASES', ',', 'NATALA', 'SA', 'NEGROS', 'ORIENTAL', ';', '1', 'ANG', 'NAMATAY', 'Nakatala', 'og', 'kinatibuk-ang', '314', 'ka', 'mga', 'kaso', 'sa', 'dengue', 'ang', 'probinsya', 'sa', 'Negros', 'Oriental', 'gikan', 'sa', 'nagkalain-lain', 'disease', 'reporting', 'units', '(', 'DRUs', ')', 'gikan', 'January', '1', 'hangtod', 'March', '18', ',', '2023.', 'Usa', 'sab', 'sa', 'maong', 'mga', 'kaso', 'ang', 'natala', 'nga', 'namatay.', 'Mas', 'taas', 'kini', 'og', '109', '%', 'kung', 'itandi', 'sa', 'samang', 'panahon', 'sa', 'niaging', 'tuig', 'nga', 'aduna'y', '150', 'cases', 'ug', '1', 'death.', 'Anaa', 'sa', '0-77', 'anyos', 'ang', 'edad', 'sa', 'natala', 'nga', 'mga', 'kaso', ',', 'diin', 'kalagmitang', 'maapektaran', 'kadtong', 'mga', 'nag-edad', 'og', '1-10', 'anyos', '(', '46', '%', ')', 'ug', 'kadaghanan', 'nila', 'mga', 'lalaki', '(', '55', '%', ')', '.', 'Ang', 'mosunod', 'mao', 'ang', 'Top', '10', 'Municipality', '/', 'City', 'sa', 'probinsya', 'nga', 'aduna'y', 'taas', 'nga', 'kaso', 'sa', 'dengue', ':', 'Dumaguete', 'City', ',', 'La', 'Libertad', ',', 'Bais', 'City', ',', 'Sibulan', ',', 'Manjuyod', ',', 'Guihulngan', 'City', ',', 'Ayungon', ',', 'Tanjay', 'City', ',', 'Bayawan', 'City', ',', 'ug', 'Sta.', 'Catalina', '.'] 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, 0, 0, 0, 0, 5, 6, 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, 0, 0, 0, 0, 0, 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, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 6, 0, 5, 6, 0, 0, 5, 6, 0]
|
cebuaner
|
4,174
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakita', 'sa', 'duha', 'ka', 'mga', 'hiker', 'ang', 'Visayan', 'leopard', 'cat', ',', 'nga', 'lokal', 'nga', 'nailhan', 'isip', '"', 'Maral', ',', '"', 'sa', 'Mt.', 'Talinis', 'sa', 'Negros', 'Oriental', 'niadtong', 'Jan.', '2', ',', '2023.', 'Usa', 'sa', 'mga', 'hikers', ',', 'Jovy', 'Tuting', ',', 'ang', 'nikuha', 'og', 'litrato', 'sa', 'samaran', 'daw', 'nga', 'Maral', 'nga', 'nagtago', 'sa', 'mga', 'dahon', 'diin', 'sila', 'niagi.', 'Ang', 'maong', ''specie', ''', ',', 'usa', 'ka', 'lumad', 'sa', 'isla', 'sa', 'Negros', ',', 'Cebu', ',', 'ug', 'Panay', '.'] 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, 8, 8, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 5, 0, 5, 0, 0, 5, 0]
|
cebuaner
|
4,175
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'COUPLE', 'NGA', 'LDR', ',', 'PWEDE', 'NANG', 'MAGHALOK', 'GAMIT', 'ANG', 'USA', 'KA', 'DEVICE', 'NGA', 'ADUNAY', 'BA-BA', 'Posible', 'nang', 'maghalok', 'ang', 'mga', 'couple', 'nga', 'anaa', 'sa', 'long-distance', 'relationship', 'human', 'makahimo', 'ang', 'mga', 'estudyante', 'sa', 'China', 'og', 'device', 'nga', 'aduna'y', '"', 'lips', '"', 'o', 'ba-ba', 'ug', 'sensors', 'nga', 'kayang', 'suhiron', 'ang', 'lihok', 'ug', 'temperatura', 'sa', 'halok', 'sa', 'naggamit', 'niini.', 'Gihimo', 'ang', 'maong', 'kahimanan', 'sa', 'mga', 'estudyante', 'sa', 'Changzhou', 'Vocational', 'Institute', 'of', 'Mechatronic', 'Technology', 'sa', 'China.', 'Aron', 'magamit', ',', 'isaksak', 'lang', 'ang', 'maong', 'device', 'sa', 'charging', 'port', 'sa', 'smartphone', 'ug', 'paandaron', 'gamit', 'ang', 'usa', 'ka', 'app.', 'Hinimo', 'sa', 'silicon', 'ang', 'ba-ba', 'niini', ',', 'nga', 'mahimong', 'gamiton', 'samtang', 'magka-video', 'call', 'ang', 'manag-uyab', 'aron', 'makita', 'ug', 'madunggog', 'nila', 'ang', 'usa'g', 'usa', '.'] 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, 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, 3, 4, 4, 4, 4, 4, 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]
|
cebuaner
|
4,176
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['IMAHE', 'SA', 'PATAYNG', 'LAWAS', 'NI', 'JESUS', 'NGA', 'GIDALA', 'SA', 'USA', 'KA', 'PARI', ',', 'NASAYPAN', 'KUNO', 'NGA', 'LALAKING', 'GI-SALVAGE', 'Usa', 'ka', 'pari', 'nga', 'papauli', 'na', 'unta', 'ang', 'gipahunong', 'sa', 'usa', 'ka', 'checkpoint', 'sa', 'kapulisan', 'human', 'nagduda', 'ang', 'mga', 'awtoridad', 'nga', 'aduna'y', 'lawas', 'sa', 'usa', 'ka', 'tawo', 'nga', 'giputos', 'og', 'plastik', 'sulod', 'sa', 'iyang', 'sakyanan.', 'Sumala', 'pa', 'sa', 'report', ',', 'gidala', 'ni', 'Fr.', 'Jonel', 'Peroy', 'sa', 'Diocese', 'of', 'Kidapawan', 'ang', 'imahe', 'ni', 'Santo', 'Entierro', 'nga', 'iyang', 'gipalit', 'sa', 'Digos', 'City', 'alang', 'sa', 'umalabot', 'nga', 'Semana', 'Santa.', 'Nangayo', 'sab', 'og', 'pasaylo', 'ang', 'kapulisan', 'ngadto', 'ni', 'Fr.', 'Peroy', '.'] 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
|
cebuaner
|
4,177
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', ',', 'nagtimaan', 'kini', 'sa', 'pagsugod', 'sa', 'Islamic', 'Holy', 'Month', 'of', 'Ramadan.', 'Usa', 'kini', 'ka', 'importanteng', 'panahon', 'sa', 'atong', 'mga', 'kaigsuonang', 'Muslim', 'aron', 'pagpalig-on', 'sa', 'ilang', 'pagtuo', 'ug', 'relasyon', 'ngadto', 'ni', 'Allah', 'pinaagi', 'sa', 'pag-ampo', 'ug', 'pagpuasa', '.'] 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, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,178
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'Local', 'and', 'Overseas', 'Job', 'Fair', 'nga', 'ipahigayon', 'ang', 'Local', 'Government', 'Unit', '(', 'LGU', ')', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'pinaagi', 'sa', 'Public', 'Employment', 'Service', 'Office', '(', 'PESO', ')', '.', 'Giimbitaran', 'ang', 'publiko', 'nga', 'motambong', 'sa', 'maong', 'kalihukan', 'karong', 'Sabado', ',', 'Apil', '1', ',', '2023', 'sa', 'Main', 'Atrium', ',', 'Robinsons', 'Place', 'sa', 'Calindagan', ',', 'Dumaguete', '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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 5, 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, 5, 6, 6, 6, 6, 0, 5, 6, 6, 6]
|
cebuaner
|
4,179
|
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', 'schedule', 'sa', 'byahe', 'sa', 'Maayo', 'Shipping', ',', 'Inc.', 'alang', 'sa', 'umalabot', 'nga', 'Semana', 'Santa.', 'Mga', 'byahe', 'kini', 'sa', 'Tampi-Bato', '&', 'Vice', 'Versa', 'ug', 'Sibulan-Liloan', '&', 'Vice', 'Versa', '.'] 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, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0]
|
cebuaner
|
4,180
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PH', ',', 'IKA-76', 'SA', 'WORLD', ''S', 'HAPPIEST', 'COUNTRIES', 'IN', '2023', 'Ni-us-os', 'ang', 'ranggo', 'sa', 'Pilipinas', 'gikan', '60', 'ngadto', 'sa', 'ika-76', 'nga', 'nasud', 'sa', 'labing', 'malipayon', 'nga', 'mga', 'nasud', 'sa', 'tibuok', 'kalibutan', ',', 'sumala', 'pa', 'sa', 'World', 'Happiness', 'Report', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,181
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Opisyal', 'nang', 'gideklarar', 'sa', 'PAGASA', 'ang', 'pagtapos', 'sa', 'panahon', 'sa', 'Amihan', 'ug', 'ang', 'pagsugod', 'sa', 'dry', 'season', 'kon', 'ting-init.', 'Gilaomang', 'molungtad', 'kini', 'hangtud', 'Mayo.', 'Tungod', 'niini', ',', 'gipasidaan', 'sa', 'PAGASA', 'ang', 'publiko', 'nga', 'mag-andam', 'sa', 'pag-init', 'sa', 'panahon', 'sa', 'mga', 'umalabot', 'nga', 'adlaw', 'ug', 'magmatngon', 'sa', 'posibleng', 'heat', 'stroke.', '#', 'NewsBite'] 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, 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]
|
cebuaner
|
4,182
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FUNERAL', 'UG', 'BURIAL', 'SERVICES', ',', 'LAKIP', 'SA', 'SENIOR', 'CITIZEN', 'DISCOUNT', 'Kinahanglang', 'hatagan', 'og', '20', '%', 'nga', 'diskwento', 'ang', '"', 'funeral', 'and', 'burial', 'services', '"', 'sa', 'mga', 'senior', 'citizen', 'ilalom', 'sa', 'Senior', 'Citizens', 'Act', ',', 'sumala', 'pa', 'sa', 'Supreme', 'Court', 'niadtong', 'Huwebes', ',', 'Mar.', '16', ',', '2023.', 'Giklaro', 'sa', 'SC', 'nga', 'sakop', 'sa', '20', '%', 'nga', 'diskwento', 'ang', 'gasto', 'sa', 'pagpalubong', 'sa', 'usa', 'ka', 'indibidwal', 'nga', 'anaa', 'sa', '60', 'anyos', 'pataas.', 'Uban', 'sa', 'bawas-presyo', 'mao', 'ang', 'mosunod', ':', 'lungon', 'o', 'urn', ',', 'pag-embalsamo', ',', 'gasto', 'sa', 'morgue', ',', 'karo', 'sa', 'patay', ',', 'paghukay', 'ug', 'pagsemento', 'sa', 'lubnganan', ',', 'ug', 'uban', 'pang', 'serbisyo', 'nga', 'gikinahanglan', 'sa', 'paghatod', 'sa', 'katapusan', 'nga', '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.
|
[0, 0, 0, 0, 0, 0, 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, 8, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,183
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', 'mao', 'ang', '#', 'WorldSleepDay.', 'Gilusad', 'kini', 'sa', 'grupo', 'sa', 'World', 'Sleep', 'Society', 'aron', 'pagpahibalo', 'sa', 'publiko', 'sa', 'importansiya', 'sa', 'pagkatulog', '.'] 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, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,184
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hingpit', 'nang', 'gidala', 'si', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'sa', 'pahulayang', 'dayon', 'niini', 'duol', 'sa', 'iyang', 'balay', 'sa', 'Barangay', 'Bonawon', 'sa', 'lungsod', 'sa', 'Siaton', 'karong', 'hapon', ',', 'Mar.', '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, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,185
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagbangutan', 'ang', 'gatusan', 'ka', 'Negrense', 'nga', 'nitambong', 'sa', 'paghatud', 'ni', 'kanhing', 'Gov.', 'Roel', 'Degamo', 'sa', 'pahulayang', 'dayon', 'niini', 'sa', 'Bonawon', ',', 'Siaton', 'karong', 'adlawa', ',', 'Mar.', '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, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,186
|
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', 'hulagway', 'sa', 'sleeker', 'spacesuits', 'nga', 'suoton', 'sa', 'mga', 'astronaut', 'nga', 'maglakaw', 'sa', 'bulan.', 'Maghatag', 'og', 'dugang', 'flexibility', 'ug', 'proteksyon', 'ang', 'maong', 'suit', 'batok', 'sa', '"', 'harsh', 'environment', '"', 'sa', 'moon', ',', 'ug', 'anaa', 'sab', 'kini', 'sa', 'nagkalain-laing', 'gidak-on', 'kon', 'sizes', '.'] 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]
|
cebuaner
|
4,187
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGROS', 'ORIENTAL', ',', 'NAGPABILING', 'GAWASNON', 'SA', 'AFRICAN', 'SWINE', 'FEVER', 'Nagpabiling', 'gawasnon', 'sa', 'African', 'Swine', 'Fever', '(', 'ASF', ')', 'ang', 'probinsya', 'sa', 'Negros', 'Oriental.', 'Mao', 'kini', 'ang', 'gibutyag', 'ni', 'Provincial', 'Head', 'Alfonso', 'Tundag', 'sa', 'Bureau', 'of', 'Animal', 'Industry', '(', 'BAI', ')', 'sa', 'usa', 'ka', 'press', 'briefing', 'sa', 'Dumaguete', 'City', 'niadtong', 'Martes', ',', 'Mar.', '14', ',', '2023.', 'Gisubli', 'sab', 'ni', 'BM', 'Woodrow', 'Maquiling', 'Sr.', 'nga', 'gipahugtan', 'sa', 'probinsya', 'ang', 'mga', 'lakang', 'niini', 'aron', 'mapunggan', 'ang', 'posibleng', 'pagsulod', 'sa', 'maong', 'sakit', 'nga', 'makaapekto', 'sa', 'mga', 'baboy', 'pinaagi', 'sa', 'usa', 'ka', 'provincial', 'ordinance.', 'Ang', 'Negros', 'Oriental', ',', 'aduna'y', 'kapin', 'P4', 'bilyon', 'nga', '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, 7, 8, 8, 8, 8, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 5, 6, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,188
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'klase', 'ug', 'walay', 'trabaho', 'sa', 'tibuok', 'Negros', 'Oriental', 'karong', 'Huwebes', ',', 'Marso', '16', ',', '2023.', 'Kini', 'subay', 'sa', 'proklamasyon', 'sa', 'Malacañang', 'aron', 'mahatagan', 'og', 'igong', 'panahon', 'ang', 'mga', 'Negrense', 'pagbangutan', 'sa', 'pagkamatay', 'ni', 'Gov.', 'Roel', 'Degamo.', 'Gitakdang', 'ihatod', 'si', 'Degamo', 'sa', 'iyang', 'pahulayang', 'dayon', 'karong', 'Huwebes', 'sa', 'hapon', 'sa', 'iyang', 'yutang', 'natawhan', 'sa', 'Bonawon', ',', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0]
|
cebuaner
|
4,189
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-share', 'sa', 'NASA', 'ang', 'usa', 'ka', 'hulagway', 'sa', 'hayag', 'ug', 'dakong', 'bituon', 'nga', 'Wolf-Rayet', ',', 'usa', 'ka', 'yugto', 'nga', 'mahitabo', 'sa', 'pipila', 'ka', 'mga', 'bituon', 'sa', 'dili', 'pa', 'sila', 'mobuto.', 'Sa', 'IG', 'post', ',', 'gibutyag', 'sa', 'maong', 'space', 'agency', 'nga', 'nahimutang', 'ang', 'bituong', 'WR', '124', 'sa', '15,000', 'light', 'years', 'ang', 'gilay-on', 'sa', 'konstelasyon', 'nga', 'Sagittarius', '.'] 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, 7, 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0]
|
cebuaner
|
4,190
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'umalabot', 'nga', 'long', 'weekend', 'karong', 'April', 'human', 'gibalhin', 'sa', 'Malacañang', 'ang', 'Araw', 'ng', 'Kagitingan', 'gikan', 'April', '9', ',', '2023', 'ngadto', 'sa', 'April', '10', ',', '2023', ',', 'human', 'sa', 'Holy', 'Week.', 'Nagpasabot', 'kini', 'nga', 'molungtad', 'ang', 'non-working', 'days', 'gikan', 'April', '6-10', ',', '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, 3, 0, 7, 8, 8, 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]
|
cebuaner
|
4,191
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nibisita', 'karong', 'hapon', ',', 'Marso', '14', ',', '2023', ',', 'si', 'Vice', 'President', 'Sara', 'Duterte', 'sa', 'haya', 'ni', 'Gov.', 'Roel', 'Degamo', 'sa', 'lungsod', '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, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 5, 0]
|
cebuaner
|
4,192
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'lima', 'ka', 'bulan', 'sa', 'International', 'Space', 'Station', ',', 'nakabalik', 'na', 'sa', 'Earth', 'ang', 'upat', 'ka', 'miyembro', 'sa', 'SpaceX', 'Crew-5', 'mission', 'sa', 'NASA.', 'Naabot', 'ang', 'mga', 'tripulante', 'ug', 'spacecraft', 'sa', 'Dragon', 'Endurance', 'sa', 'Gulf', 'sa', 'Mexico', 'niadtong', 'Sabado', ',', 'Mar.', '11', ',', '2023.', 'Ang', 'mga', 'tripulante', 'mao', 'sila', 'si', 'NASA', 'astronauts', 'Nicole', 'Mann', 'ug', 'Josh', 'Cassada', ',', 'uban', 'ni', 'JAXA', '(', 'Japan', 'Aerospace', 'Exploration', 'Agency', ')', 'astronaut', 'Koichi', 'Wakata', ',', 'ug', 'Roscosmos', 'cosmonaut', 'Anna', 'Kikina.', 'Anaa', 'sila', 'sa', 'kawanangan', 'og', 'kapin', '157', 'ka', 'adlaw', 'ug', 'naglibot', 'sa', 'Eartn', 'sa', 'kapin', '2,500', 'ka', 'higayon', 'samtang', 'nagsakay', 'sa', 'ISS', '.'] 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 1, 2, 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, 5, 0]
|
cebuaner
|
4,193
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'kuryente', 'sa', 'tibuok', 'dakbayan', 'sa', 'Dumaguete', 'ug', 'sa', '7', 'pa', 'ka', 'laing', 'dapit', 'sa', 'Negros', 'Oriental', 'karong', 'sunod', 'Sabado', ',', 'Mar.', '25', ',', '2023', ',', 'gikan', 'alas-6', 'sa', 'buntag', 'ngadto', 'sa', 'alas-6', 'sa', 'hapon.', 'Gawas', 'sa', 'Dumaguete', 'City', ',', 'apektado', 'sab', 'sa', 'maong', 'power', 'service', 'interruption', 'ang', 'Calo', ',', 'San', 'Jose', ',', 'Sibulan', ',', 'Bacong', ',', 'Valencia', ',', 'Dauin', ',', 'Zamboanguita', ',', 'ug', 'Siaton.', 'Kini', 'tungod', 'kay', 'maghimo', 'og', 'mga', 'pag-ayo', 'ug', 'maintenance', 'activities', 'ang', 'National', 'Grid', 'Corporation', 'of', 'the', 'Philippines', '(', 'NGCP', ')', 'samtang', 'ipahigayon', 'ang', 'maong', 'brownout.', 'Pag-charge', 'na', 'daan', 'sa', 'imong', 'mga', 'cellphone', 'ug', 'power', 'bank', ',', 'besh', '!'] 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, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 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]
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cebuaner
|
4,194
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahimong', 'emosyonal', 'ang', 'pagbalik', 'ni', 'Gov.', 'Guido', 'Reyes', 'sa', 'Kapitolyo', 'sa', 'Negros', 'Oriental', 'karong', 'adlawa', ',', 'Marso', '13', ',', '2023', ',', 'human', 'sa', 'iyang', 'medical', 'leave', 'sa', 'kaulohan.', 'Pag-abot', 'niini', 'sa', 'buhatan', 'sa', 'gobernador', ',', 'wala', 'kapugong', 'si', 'Reyes', 'sa', 'iyang', 'luha', 'ug', 'gisandigan', 'niini', 'ang', 'standee', 'ni', 'kanhing', 'Gov.', 'Roel', 'Degamo.', 'Bisan', 'pa', 'og', 'napagaw', ',', 'gipadayon', 'ni', 'Reyes', 'ang', 'iyang', 'unang', 'speech', 'isip', 'gobernador', 'sa', 'probinsya.', '"', 'Maningkamot', 'ra', 'ko', 'ha', ',', 'ug', 'mga', 'board', 'member', ',', 'unsay', 'programa', 'ni', 'governor', ',', 'tanan', 'akong', 'ipadayon.', 'Akong', 'ipadayon.', 'Mao', 'man', 'iyang', 'gusto', ',', '"', 'sumala', 'pa', 'niya', '.'] 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, 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, 1, 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, 0, 0, 0, 0, 0, 0, 0]
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cebuaner
|
4,195
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AIRASIA', ',', 'NAGTANYAG', 'OG', 'P1', 'NGA', 'PLITE', 'KARONG', 'MARSO', 'Nagtanyag', 'ang', 'AirAsia', 'og', 'laing', 'hugna', 'sa', 'P1', 'nga', 'seat', 'sale', 'alang', 'sa', 'mga', 'pinili', 'nga', 'local', 'destinations.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'airline', 'karong', 'adlawa', ',', 'Mar.', '13', ',', '2023.', 'Mahimong', 'mo-avail', 'sa', 'mga', 'discounted', 'flights', 'gikan', 'Mar.', '13-19', 'alang', 'sa', 'one-way', 'base', 'fare', 'paingon', 'sa', 'Boracay', ',', 'Bohol', ',', 'Puerto', 'Princesa', ',', 'Bacolod', ',', 'Davao', ',', 'Kalibo', ',', 'Cagayan', 'De', 'Oro', 'ug', 'Roxas', 'gikan', 'Manila', ',', 'Boracay', ',', 'Puerto', 'Princesa', ',', 'Davao', 'ug', 'Davao', ',', 'ug', 'Cayagan', 'de', 'Oro', 'gikan', 'Cebu-Mactan', 'International', 'Airport.', 'Samtang', ',', 'mahimo', 'sab', 'nga', 'maka-avail', 'sa', 'international', 'destination', 'tickets', 'nga', 'anaa', 'sa', 'P512', 'ngadto', 'sa', 'P2,811', 'paingon', 'sa', 'Macao', ',', 'Taipei', ',', 'Bangkok', ',', 'Bali', ',', 'Tokyo', 'ug', 'Osaka', 'alang', 'sa', 'biyahe', 'gikan', 'Sept.', '4', ',', '2023', 'ngadto', 'sa', 'Aug.', '13', ',', '2024', '.'] 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, 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, 3, 0, 3, 0, 3, 4, 0, 3, 0, 3, 0, 3, 0, 3, 4, 4, 0, 3, 0, 3, 0, 3, 0, 3, 4, 0, 3, 0, 3, 0, 0, 3, 4, 4, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,196
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PH', 'ARMY', ',', 'NAGPADALA', 'OG', 'DUGANG', 'NGA', 'TROPA', 'SA', 'NEGOR', 'HUMAN', 'SA', 'PAGPATAY', 'NI', 'GOV.', 'ROEL', 'DEGAMO', 'Unom', 'ka', 'batalyon', 'sa', 'Philippine', 'Army', 'ang', 'gipadala', 'sa', 'Negros', 'Oriental', 'niadtong', 'Domiggo', ',', 'usa', 'ka', 'semana', 'ang', 'nilabay', 'human', 'sa', 'pagpatay', 'ni', 'kanhi', 'Gov.', 'Roel', 'Degamo.', 'Gilangkuban', 'ang', 'mga', 'tropa', 'sa', '11th', ',', '94th', ',', '79th', ',', '62nd', ',', '15th', ',', 'ug', '47th', 'Infantry', 'Battalions.', 'Ipakatap', 'ang', 'mga', 'personahe', 'sa', 'kasundalohan', 'sa', 'ikaduha', 'ug', 'ikatulo', 'nga', 'distrito', 'sa', 'Negros', 'Oriental', 'isip', 'parte', 'sa', 'pagpaningkamot', 'sa', 'gobyerno', 'nga', 'pakusgan', 'ang', 'joint', 'law', 'enforcement', 'operations', 'uban', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'sa', 'probinsya.', 'Sumala', 'pa', 'ni', 'AFP', 'Visayas', 'Command', '(', 'VISCOM', ')', 'Commander', 'Lt.', 'Gen.', 'Benedict', 'Arevalo', ',', 'makigtinabangay', 'sa', 'PNP', 'ang', 'mga', 'gipadala', 'nga', 'tropa', 'aron', 'masikop', 'ang', 'mga', 'nipatay', 'ni', 'Degamo', 'ug', 'sa', 'uban', 'pang', 'mga', 'biktima', 'nga', 'wala', 'pa', 'nadakpan', '.'] 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, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 4, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 0, 3, 4, 4, 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 1, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,197
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihatod', 'na', 'sa', 'iyang', 'hometown', 'sa', 'Bonawon', ',', 'Siaton', 'ang', 'patayng', 'lawas', 'ni', 'anhing', 'Governor', 'Roel', 'Degamo.', 'Samtang', 'gihatod', 'kini', 'didto', ',', 'nanggawas', 'ang', 'mga', 'residente', 'sa', 'mga', 'lungsod', 'sa', 'Bacong', 'ug', 'Dauin', 'gisugat', 'ang', 'iyang', 'lungon.', 'Nagpagawas', 'sab', 'sila', 'og', 'mga', 'mensahe', 'sa', 'pagbangutan', 'ug', 'pagpasalamat', 'sa', 'napatay', 'nga', 'gobernador', '.'] 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, 6, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]
|
cebuaner
|
4,198
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nibisita', 'karong', 'adlawa', '(', 'Mar.', '11', ',', '2023', ')', 'si', 'Defense', 'Secretary', 'Carlito', 'Galvez', 'Jr.', ',', 'Senator', 'Mark', 'Villar', ',', 'ug', 'AFP', 'Chief', 'of', 'Staff', 'Gen.', 'Andres', 'Centino', 'sa', 'ikaduhang', 'adlaw', 'sa', 'haya', 'ni', 'Gov.', 'Roel', 'Degamo', 'sa', 'Negros', 'Oriental', 'Provincial', 'Capitol', 'ning', '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, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 1, 2, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 6, 6, 0, 0, 0, 5, 0]
|
cebuaner
|
4,199
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGOR', ',', 'GI-BAN', 'SAB', 'ANG', 'PORK', 'PRODUCTS', 'GIKAN', 'SA', 'CEBU', 'Gipahamtang', 'sa', 'provincial', 'government', 'sa', 'Negros', 'Oriental', 'ang', 'total', 'ban', 'sa', 'baboy', 'ug', 'uban', 'pang', 'produkto', 'gikan', 'sa', 'probinsya', 'sa', 'Cebu', 'human', 'nakatala', 'sa', 'unang', 'kaso', 'sa', 'African', 'Swine', 'Fever', 'sa', 'Carcar', 'City.', 'Mo-epekto', 'ang', 'total', 'ban', 'sa', 'mosunod', 'nga', '45', 'ka', 'adlaw', 'base', 'sa', 'usa', 'ka', 'executive', 'order', 'nga', 'gi-isyu', 'niadtong', 'Mar.', '3.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'chief', 'operations', 'officer', 'sa', 'Provincial', 'Veterinary', 'Office', '(', 'PVO', ')', 'nga', 'si', 'Ophelia', 'Felisilda.', 'Niadtong', 'Miyerkules', ',', 'gikompirma', 'sa', 'Bureau', 'of', 'Animal', 'Industry', '(', 'BAI', ')', 'sa', 'Region', '7', 'ang', 'unang', 'kaso', 'sa', 'ASF', 'sa', 'Carcar', 'City', ',', 'human', '58', 'sa', '149', 'ka', 'pig', 'blood', 'samples', 'ang', 'nagpositibo', 'niadtong', 'Mar.', '1', '.'] 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, 5, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 7, 8, 8, 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, 3, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 0, 7, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
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