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4,400
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MINIMUM', 'WAGE', 'SA', 'NCR', ',', 'GIDUSO', 'NGA', 'IPATAAS', 'NGADTO', 'SA', 'P1,140', 'KADA', 'ADLAW', 'TUNGOD', 'SA', 'INFLATION', 'Aron', 'mabuhi', 'nga', '"', 'disente', '"', 'ang', 'usa', 'ka', 'pamilya', 'sa', 'Metro', 'Manila', 'nga', 'aduna'y', 'lima', 'ka', 'sakop', ',', 'kinahanglan', 'nila', 'og', 'P25,000', 'kada', 'bulan', 'tungod', 'sa', 'paspas', 'nga', 'pagsaka', 'sa', 'presyo', 'sa', 'mga', 'palaliton.', 'Gipagawas', 'sa', 'IBON', 'Foundation', 'ang', 'maong', 'estimasyon', 'niadtong', 'Martes', ',', 'susamang', 'adlaw', 'diin', 'gibalita', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'nga', 'anaa', 'sa', '8', '%', 'ang', 'inflation', 'rate', 'niadtong', 'Nobyembre', '--', 'pinakataas', 'sa', 'kapin', '14', 'ka', 'tuig.', 'Layo', 'kini', 'sa', 'P570', 'nga', 'minimum', 'wage', 'sa', 'Metro', 'Manila', ',', 'nga', 'mao'y', 'pinakataas', 'sa', 'tibuok', 'nasud.', 'Kung', 'nagsaka', 'ang', 'presyo', 'sa', 'mga', 'palaliton', 'apan', 'wala', 'nisaka', 'ang', 'sweldo', ',', 'mas', 'mogamay', 'sab', 'ang', 'tinuod', 'nga', 'bili', 'niini.', 'Mas', 'mogamay', 'ang', 'pwedeng', 'mapalit', 'sa', 'susamang', 'kantidad', 'sa', 'kwarta', 'sa', 'matag', 'higayon', 'nga', 'mahitabo', 'kini', '.'] 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
|
4,401
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LPA', 'NASIGPATAN', 'GAWAS', 'SA', 'PAR', ',', 'POSIBLENG', 'MAHIMONG', 'BAGYO', 'SA', 'MGA', 'MOSUNOD', 'NGA', 'ADLAW', 'Gibantayan', 'na', 'karon', 'ang', 'usa', 'ka', 'low', 'pressure', 'area', '(', 'LPA', ')', 'nga', 'nasigpatan', 'gawas', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'karong', 'adlawa', ',', 'Dec.', '6', ',', '2022.', 'Ulahi', 'kining', 'nasuta', 'sa', 'kadagatan', 'dapit', 'sa', 'sidlakang', 'bahin', 'sa', 'Mindanao.', 'Posibleng', 'mosulod', 'kini', 'sa', 'PAR', 'sa', 'mosunod', 'nga', '24-48', 'oras.', 'Wala', 'sab', 'giwala', 'ang', 'posibilidad', 'nga', 'mokusog', 'kini', 'ug', 'mahimong', 'bagyo', 'sa', 'mga', 'mosunod', 'nga', 'adlaw.', 'Kun', 'ugaling', 'mahimo', 'kining', 'bagyo', 'sulod', 'sa', 'PAR', ',', 'nganlan', 'kini', 'sa', 'PAGASA', 'nga', '#', 'RosalPH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 5, 0, 0, 0, 0, 3, 0, 0, 7, 0]
|
cebuaner
|
4,402
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LUNGON', ',', 'GRAND', 'PRIZE', 'SA', 'RAFFLE', 'SA', 'USA', 'KA', 'CHRISTMAS', 'PARTY', 'Lungon', 'ang', 'gihimong', 'grand', 'prize', 'sa', 'raffle', 'sa', 'Christmas', 'party', 'sa', 'grupong', 'Philippine', 'Mortuary', 'Association.', 'Gawas', 'sa', 'lungon', ',', 'lakip', 'sab', 'sa', 'premyo', 'ang', 'formaldehyde', 'nga', 'pang-embalsamo', 'ug', 'mga', 'marble', 'urn.', 'Sumala', 'pa', 'sa', 'report', ',', 'malipayon', 'ang', 'mga', 'nakadaog', 'sa', 'premyo', 'tungod', 'mahal', 'ang', 'presyo', 'sa', 'lungon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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]
|
cebuaner
|
4,403
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAODNON', 'NGA', 'NAGTINGUHANG', 'PAUBSAN', 'ANG', 'EDAD', 'SA', 'SENIOR', 'CITIZEN', ',', 'GIDUSO', 'Gipasaka', 'ni', 'Senator', 'Bong', 'Revilla', 'ang', 'usa', 'ka', 'balaudnon', 'nga', 'nagtinguhang', 'paubsan', 'ang', 'kwalipikadong', 'edad', 'aron', 'legal', 'nga', 'makonsiderar', 'isip', 'usa', 'ka', 'senior', 'citizen', 'sa', 'nasud', ',', 'gikan', '60', 'anyos', 'ngadto', 'sa', '56', 'anyos.', 'Sumala', 'pa', 'ni', 'Sen.', 'Revilla', ',', 'parte', 'ang', 'maong', 'balaudnon', 'sa', 'iyang', 'agenda', 'sa', 'pagpasiugda', 'sa', 'social', 'justice', 'legislation', 'nga', 'makabenepisyo', 'ang', 'kadaghanan', 'sa', 'mga', 'Pilipino', ',', 'ilabi', 'na', 'kadtong', '"', 'the', 'least', ',', 'the', 'lost', ',', 'and', 'the', 'last.', '"', 'Dugang', 'pa', 'niya', ',', 'ang', 'mga', 'Pilipino', 'nga', 'apil', 'sa', 'maong', 'age', 'group', 'nagtrabaho', 'alang', 'sa', 'kaayuhan', 'sa', 'ilang', 'pamilya.', 'Gisubli', 'sab', 'niya', 'ang', 'bililhong', 'kontribusyon', 'sa', 'mga', 'tigulang', 'alang', 'sa', 'kalamboan', 'sa', 'nasud.', 'Tumong', 'sa', 'Senate', 'Bill', '1573', 'ang', 'pag-amendar', 'sa', 'Republic', 'Act', '7432', 'nga', 'nagpasabot', 'nga', 'ang', 'senior', 'citizen', 'mao', 'ang', '"', 'any', 'resident', 'citizen', 'of', 'the', 'Philippines', 'at', 'least', 'sixty', '(', '60', ')', 'years', 'old.', '"', 'Kung', 'maaprobahan', ',', 'tanan', 'nga', 'mga', 'residenteng', 'lungsuranon', 'sa', 'nasud', 'nga', 'nag-edad', 'og', '56', 'anyos', 'mahimo', 'ng', 'makonsiderar', 'isip', 'usa', 'ka', 'senior', 'citizen', ',', 'ug', 'aduna', 'na'y', 'katungod', 'sa', 'mga', 'benepisyo', 'nga', 'gihatag', 'alang', 'kanila', '.'] 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
|
4,404
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BABAYI', 'SA', 'MEXICO', ',', 'PATAY', 'HUMAN', 'GIHIWA', 'ANG', 'TIYAN', 'ARON', 'KAWATON', 'ANG', 'WALA', 'PA', 'MATAWO', 'NGA', 'BATA', 'Usa', 'ka', 'mabdos', 'sa', 'Mexico', 'ang', 'namatay', 'human', 'gihiwa', 'ang', 'iyang', 'tiyan', 'sa', 'duha', 'sa', 'mamumuno', 'kinsa', 'gikawat', 'ang', 'iyang', 'wala', 'pa', 'matawo', 'nga', 'bata', ',', 'sumala', 'pa', 'sa', 'mga', 'awtoridad', 'niadtong', 'Lunes.', 'Nasikop', 'ang', 'duha', 'ka', 'giingong', 'mamumuno', 'uban', 'sa', 'bag-ong', 'natawo', 'nga', 'bata', 'nga', 'anaa', 'sa', 'ilang', 'posesyon.', 'Ang', 'mga', 'suspek', ',', 'usa', 'ka', 'babayi', 'ug', 'usa', 'ka', 'lalaki.', 'Niatubang', 'kini', 'sila', 'sa', 'huwes', 'niadtong', 'Lunes', 'ug', 'giakusahan', 'sa', 'kasong', 'kidnapping', 'ug', 'femicide.', 'Sumala', 'pa', 'sa', 'usa', 'ka', 'opisyal', ',', 'giingong', 'gihiwa', 'sa', 'mga', 'suspek', 'ang', 'biktima', 'aron', 'makuha', 'ang', 'fetus', 'tungod', 'ang', 'babayi', 'nga', 'suspek', 'dili', 'makabaton', 'og', 'anak.', 'Gibutyag', 'sab', 'sa', 'mga', 'paryente', 'nga', 'gidani', 'ang', 'biktima', 'pinaagi', 'sa', 'social', 'media', 'uban', 'sa', 'saad', 'nga', 'aduna'y', 'mga', 'sinina', 'para', 'sa', 'iyang', 'bata.', 'Mao', 'na', 'kini', 'ang', 'ikatulong', 'kaso', 'sa', 'susamang', 'panghitabo', 'sa', 'mga', 'niaging', 'tuig.', 'Kapin', '3,700', 'ka', 'mga', 'babayi', 'ang', 'gipatay', 'sa', 'violence-plagued', 'Mexico', 'niadtong', '2021', ',', 'diin', 'mga', '1,000', 'ang', 'giklasipikar', 'isip', 'femicides', '.'] 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
|
4,405
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Boluntaryo', 'ang', 'mga', 'Christmas', 'party', 'sa', 'mga', 'tunghaan', 'ug', 'opisina', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', '.', 'Sumala', 'pa', 'sa', 'DepEd', 'Order', 'No.', '52', ',', 'gidila', 'sab', 'ang', 'solicitations', 'o', 'pagpangolekta', 'og', 'kwarta', '.'] 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, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,406
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KA', 'METEOR', 'SHOWER', ',', 'MAKITA', 'SA', 'PILIPINAS', 'KARONG', 'DISYEMBRE', 'Makita', 'ang', 'Geminid', 'Meteor', 'Shower', 'sugod', 'Disyembre', '4-17', 'nga', 'ang', 'peak', 'sa', 'Disyembre', '14', ',', 'alas-7', 'sa', 'gabii.', 'Makita', 'sab', 'ang', 'Ursid', 'Meteor', 'Shower', 'sa', 'Disyembre', '17-26', 'nga', 'ang', 'peak', 'sa', 'Disyembre', '23.', 'Sumala', 'pa', 'sa', 'PAGASA', ',', 'dili', 'kinahanglan', 'nga', 'magdala', 'og', 'mga', 'telescope', 'o', 'binoculars', 'ang', 'mga', 'ganahan', 'nga', 'makakita', 'sa', 'meteor', 'shower', 'tungod', 'makita', 'kini', 'sa', ''naked', 'eye', ''', '.'] 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
|
4,407
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KAWATAN', ',', 'NALANDIG', 'SA', 'PRISOHAN', 'HUMAN', 'MABALIGYA', 'SA', 'TAG-IYA', 'ANG', 'GIKAWAT', 'NILA', 'NGA', 'MOTORSIKLO', 'Duha', 'ka', 'mga', 'kawatan', 'ang', 'nalandig', 'sa', 'prisohan', 'human', 'nila', 'nabaligya', 'ang', 'nakawat', 'nga', 'motorsiklo', 'sa', 'tinuod', 'nga', 'tag-iya', 'niini', 'niadtong', 'Biyernes', ',', 'Disyembre', '2', ',', '2022.', 'Nadakpan', 'sa', 'kapulisan', 'sila', 'si', 'Mosram', 'Adam', 'ug', 'Sul', 'Kipos', ',', 'puros', 'lumolupyo', 'sa', 'lungsod', 'sa', 'Pitik', 'sa', 'probinsya', 'sa', 'Cotabato', ',', 'sa', 'usa', 'ka', 'entrapment', 'operation.', 'Kini', 'human', 'nga', 'nangayo', 'og', 'tabang', 'si', 'Alliudin', 'Aliman', ',', 'tag-iya', 'sa', 'gikawat', 'nga', 'motorsiklo.', 'Wala', 'kahibalo', 'ang', 'duha', 'nga', 'si', 'Aliman', 'ang', 'tinuod', 'nga', 'tag-iya', 'sa', 'motorsiklo', 'nga', 'ilang', 'gikawat', 'pipila', 'ka', 'adlaw', 'ang', 'nilabay', 'samtang', 'naka-park', 'kini', 'sa', 'dalan', 'sa', 'Barangay', 'Rosary', 'Heights', '3.', 'Giplano', 'ang', 'entrapment', 'operation', 'human', 'nahibal-an', 'ni', 'Aliman', 'nga', 'gi-post', 'nila', 'ni', 'Adam', 'ug', 'Kipos', 'ang', 'litrato', 'sa', 'iyang', 'nawala', 'nga', 'motorsiklo', 'ug', 'gi-caption', 'nga', 'kini', 'ibaligya', ',', 'uban', 'sa', 'ilang', 'contact', 'numbers', 'alang', 'sa', 'mga', 'interesado.', 'Gilimbong', 'ni', 'Aliman', 'sila', 'si', 'Adam', 'ug', 'Kipos', 'pinaagi', 'sa', 'pagpakaaron-ingon', 'nga', 'interesado', 'siya', 'sa', 'deal', ',', 'uban', 'sa', 'tabang', 'sa', 'Bravo', 'ug', 'mga', 'sakop', 'niini.', 'Human', 'sa', 'entrapment', 'operation', ',', 'nabawi', 'ni', 'Aliman', 'ang', 'iyang', 'motorsiklo.', 'Natanggong', 'na', 'ang', 'duha', 'ka', 'suspek', 'sa', 'police', 'detention', 'facility', 'kinsa', 'nagpaabot', 'sa', 'prosekusyon', '.'] 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
|
4,408
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P88', 'NGA', 'PLITE', 'HANGTOD', 'DISYEMBRE', '15', ',', '2022', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'laing', 'promo', 'nga', 'P88', 'nga', 'one-way', 'base', 'fares', 'gikan', 'sa', 'Manila', 'paingon', 'sa', 'Davao', ',', 'Bacolod', ',', 'ug', 'Kalibo', 'hangtod', 'sa', 'Disyembre', '15', ',', '2022.', 'Mahimo', 'sab', 'mga', 'mobisita', 'sa', 'mga', 'local', 'spots', 'sama', 'sa', 'Boracay', ',', 'Bohol', ',', 'ug', 'Puerto', 'Prinsesa', 'nga', 'ingon', 'kaubos', 'sa', 'P288', 'nga', 'plite.', 'Sumala', 'pa', 'sa', 'CEB', ',', 'ang', 'maong', 'one-way', 'base', 'fares', '"', 'are', 'inclusive', 'of', '7', 'kg', 'hand-carry', 'baggage', 'allowance', ',', 'but', 'exclusive', 'of', 'web', 'admin', 'fee', ',', '12', '%', 'VAT', ',', 'terminal', 'fees', ',', 'and', 'fuel', 'surcharge.', '"', 'Aron', 'pag-book', 'sa', 'biyahe', ',', 'mahimong', 'bisitahon', 'ang', 'seat', 'sale', 'section', 'sa', 'opisyal', 'nga', 'website', 'sa', 'CEB', 'ug', 'i-click', 'ang', '"', 'book', 'now', '"', 'tapad', 'sa', 'napili', 'nga', 'destinasyon', 'sa', 'Pilipinas.', 'Hangtod', 'May', '13', ',', '2023', 'ang', 'travel', 'period', 'sa', 'maong', 'promo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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]
|
cebuaner
|
4,409
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BABAYI', 'SA', 'ESTADOS', 'UNIDOS', 'GISULAY', 'PAG-ABRI', 'ANG', 'PURTAHAN', 'SA', 'EROPLANO', 'SAMTANG', 'NAGBIYAHE', ',', 'NIINGON', 'NGA', 'SI', 'JESUS', 'ANG', 'NAGPABUHAT', 'NIYA', 'Usa', 'ka', 'babayi', 'ang', 'nisulay', 'pag-abri', 'sa', 'purtahan', 'sa', 'eroplanong', 'iyang', 'gisakyan', 'sa', 'tunga-tunga', 'sa', 'ilang', 'biyahe', ',', 'ug', 'niingon', 'nga', 'gihangyo', 'siya', 'ni', 'Jesus', 'aron', 'buhaton', 'kini.', 'Tungod', 'niini', ',', 'nag-emergency', 'landing', 'ang', 'eroplano', 'aron', 'masigurado', 'nga', 'luwas', 'ang', 'tanang', 'sakay', 'niini.', 'Sumala', 'pa', 'sa', 'mga', 'awtoridad', 'sa', 'Houston', ',', 'nagsugod', 'ang', 'insidente', 'sa', 'dihang', 'gitutukan', 'sa', '34-anyos', 'nga', 'babayi', 'ang', 'exit', 'door', 'ug', 'nihangyo', 'kung', 'pwede', 'ba', 'niyang', 'ma-enjoy', 'ang', 'talan-awon', 'gikan', 'sa', 'dapit', 'diin', 'anaa', 'ang', 'mga', 'flight', 'attendants.', 'Ngadtong', 'gitubag', 'na', 'siya', 'nga', 'dili', 'pwede', ',', 'giingong', 'nipugos', 'ang', 'babayi', 'ug', 'gisugdan', 'pagbira', 'ang', 'exit', 'door', 'handle.', 'Gipaak', 'sab', 'niya', 'ang', 'paa', 'sa', 'usa', 'ka', 'pasahero', 'kinsa', 'nisulay', 'pagpugong', 'niya.', 'Human', 'sa', 'maong', 'panghitabo', ',', 'gidala', 'sa', 'kustodiya', 'sa', 'mga', 'awtoridad', 'ang', 'maong', 'babayi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,410
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['E-TRAVEL', 'GILUSAD', 'ALANG', 'SA', 'MAS', 'SAYON', 'NGA', 'PAGSULOD', 'SA', 'MGA', 'BIYAHERO', 'SA', 'PILIPINAS', 'Gilusad', 'ang', 'usa', 'ka', 'online', 'registration', 'system', 'alang', 'sa', 'mga', 'biyahero', 'ug', 'namalik', 'nga', 'mga', 'residente', 'sa', 'Pilipinas.', 'Tumong', 'niini', 'nga', 'mapahapsay', 'ang', 'pagsulod', 'sa', 'nasud', 'ug', 'pagkolekto', 'sa', 'mga', 'datos.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'Office', 'of', 'the', 'Press', 'Secretary', 'niadtong', 'Biyernes', ',', 'Disyembre', '2', ',', '2022.', 'Sumala', 'pa', 'sa', 'OPS', ',', 'ma-access', 'ang', 'Health', 'Declaration', 'Checklist', 'sa', 'Department', 'of', 'Health-Bureau', 'of', 'Quarantine', 'pinaagi', 'sa', 'eTravel', 'platform', 'sugod', 'niadtong', 'Disyembre', '2.', 'Gisubli', 'sab', 'sa', 'OPS', 'nga', 'kadtong', 'mga', 'pasahero', 'nga', 'naggamit', 'pa', 'sa', 'kaniadto', 'www.onehealthpass.com.ph', ',', 'mapunta', 'sa', 'bag-ong', 'eTravel', 'domain', 'sugod', 'Disyembre', '5', ',', 'ang', 'karaan', 'nga', 'domain', 'sa', 'One', 'Health', 'Pass', 'dili', 'na', 'mahimong', 'ma-access', 'sa', 'publiko.', 'Ang', 'pagrehistro', 'sa', 'eTravel', 'platform', ',', 'libre', 'alang', 'sa', 'tanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 7, 8, 8, 0, 3, 4, 4, 4, 4, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,411
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['VIVA', 'MAGENTA', ',', 'GIDEKLARAR', 'SA', 'PANTONE', 'ISIP', 'COLOR', 'OF', 'THE', 'YEAR', 'ALANG', 'SA', '2023', 'Gideklarar', 'ang', 'Viva', 'Magenta', '18-1750', 'isip', 'Color', 'of', 'the', 'Year', 'alang', 'sa', 'tuig', '2023', ',', 'mao', 'kini', 'ang', 'gianunsyo', 'sa', 'Pantone', 'niadtong', 'Biyernes', ',', 'Disyembre', '2', ',', '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.
|
[7, 8, 0, 0, 0, 3, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,412
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'pawikan', 'ang', 'napalgang', 'patay', 'dapit', 'sa', 'Silliman', 'Beach', 'sa', 'Barangay', 'Bantayan', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'gabii', ',', 'Dec.', '1', ',', '2022.', 'Gikatahong', 'taod-taod', 'nang', 'namatay', 'ang', 'maong', 'pawikan', 'ug', 'gilubong', 'na', 'kini', 'sa', 'mga', 'residente', 'sa', 'maong', 'dapit.', 'Wala', 'pa', 'matino', 'sa', 'pagkakaron', 'kung', 'unsay', 'hinungdan', 'sa', 'pagkamatay', 'sa', 'maong', 'pawikan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,413
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagmaya', 'karon', 'ang', 'pamilya', 'Ramirez', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'human', 'nga', 'parehong', 'nakatapos', 'isip', 'Top', '1', 'ang', 'ilang', 'magsuon', 'sa', 'tagsa-tagsa', 'ka', 'board', 'exam', 'nga', 'ilang', 'gikuha.', 'Kini', 'human', 'nga', 'nakuha', 'sa', 'ilang', 'kinamanghuran', 'nga', 'si', 'Abigail', 'Ramirez', 'ang', 'First', 'Place', 'sa', 'November', '2022', 'Nurse', 'Licensure', 'Examination.', 'Si', 'Ramirez', 'nigraduwar', 'sa', 'St.', 'Paul', 'University', 'Dumaguete', 'isip', 'magna', 'cum', 'laude.', 'Niadtong', 'Nobyembre', '2017', ',', 'Top', '1', 'sab', 'ang', 'iyang', 'magulang', 'nga', 'si', 'Alec', 'Benjamin', 'sa', 'Geologist', 'Licensure', 'Examination.', 'Siya', 'usa', 'ka', 'produkto', 'sa', 'Negros', 'Oriental', 'State', 'University', 'ug', 'nagtudlo', 'karon', 'sa', 'National', 'Institute', 'of', 'Geological', 'Sciences', '(', 'NIGS', ')', 'sa', 'University', 'of', 'the', 'Philippines', 'Dilliman.', 'Parehong', 'alumni', 'ang', 'magsuong', 'Ramirez', 'sa', 'Ramon', 'Teves', 'Pastor', 'Memorial-Dumaguete', 'Science', 'High', 'School', ',', 'usa', 'sa', 'mga', 'inilang', 'public', 'high', 'school', 'ning', 'dakbayan', '.'] 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, 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, 1, 2, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 1, 0, 0, 3, 4, 4, 4, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 7, 8, 8, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,414
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FENG', 'SHUI', 'EXPERT', ':', '2023', 'MAO', 'ANG', 'TUIG', 'SA', 'PAG-MOVE', 'ON', 'Ang', '2023', 'mao'y', 'tuig', 'alang', 'sa', 'pag-move', 'on', 'ug', 'pag-let', 'go', ',', 'sumala', 'pa', 'sa', 'usa', 'ka', 'feng', 'shui', 'expert', 'niadtong', 'Lunes', ',', 'Nobyembre', '28', ',', '2022.', 'Gipasabot', 'ni', 'feng', 'shui', 'practitioner', 'Marites', 'Allen', ',', 'ang', 'kasamtangang', '20-year', 'feng', 'shui', 'period', 'mao', 'ang', 'gitawag', 'nga', 'Period', 'of', '8', ',', 'nga', 'nagsugod', 'niadtong', 'Pebrero', '2004', 'ug', 'matapos', 'sa', 'Pebrero', '2024.', 'Sumala', 'pa', 'ni', 'Allen', ',', 'ang', '2023', 'mao', 'ang', 'pinakamaayong', 'panahon', 'sa', 'pagsalig', 'sa', 'imong', 'gibati', 'ug', 'pag-move', 'on.', 'Gisubli', 'ni', 'Allen', 'nga', 'aduna'y', 'mga', 'tawo', 'nga', 'mopadayon', 'pa', 'tungod', 'sa', 'ideya', 'sa', 'fate', 'ug', 'destiny.', 'Gitambag', 'sab', 'ni', 'Allen', 'nga', 'mahimo', 'pang', 'sulayan', ',', 'apan', 'kung', 'dili', 'gyud', 'mogana', ',', 'dili', 'na', 'gyud.', 'Mao', 'na', 'siya'y', 'panahon', 'sa', 'pag-move', 'on.', 'Ang', 'tuig', '2023', 'mao', 'ang', 'Year', 'of', 'the', 'Rabbit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 8, 0, 0, 0, 0, 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, 7, 8, 0, 1, 2, 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, 1, 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, 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, 7, 8, 8, 8, 0]
|
cebuaner
|
4,415
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FRIENDSTER', 'IS', 'NOT', 'BACK', ':', 'DICT', 'NAGPASIDAAN', 'SA', 'PAGREHISTRO', 'SA', 'SITE', 'NGA', 'GIGAMIT', 'PARA', 'PHISHING', 'Nagpasidaan', 'ang', 'Department', 'of', 'Information', 'and', 'Communications', 'Technology', '(', 'DICT', ')', 'sa', 'publiko', 'nga', 'aduna'y', 'viral', 'phishing', 'site', 'nga', 'nagpakaaron-ingnon', 'nga', 'link', 'sa', 'gibag-ong', 'version', 'sa', 'karaan', 'nga', 'social', 'network', 'nga', 'Friendster.', 'Usa', 'ka', 'Facebook', 'post', 'nga', 'nag-ingon', 'nga', 'mahimo', 'ng', 'morehistro', 'sa', 'nabanhaw', 'nga', 'Friendster', 'ang', 'nakaangkon', 'og', 'interes', 'sa', 'mga', 'netizens', 'niadtong', 'katapusan', 'sa', 'semana', ',', 'diin', 'nakakuha', 'kini', 'og', '9,000', 'shares', 'ug', '6,000', 'reactions', 'sa', 'pagsulat', 'ning', 'balita.', 'Nagpasidaan', 'ang', 'National', 'Computer', 'Emergency', 'Response', 'Team', '(', 'NCERT', ')', ',', 'usa', 'ka', 'division', 'ilalom', 'sa', 'DICT', 'Cybersecurity', 'Bureau', ',', 'nga', 'bisan', 'paman', 'murag', 'tinuod', 'ang', 'website', ',', 'ang', 'IP', 'address', 'niini', 'aduna'y', 'niaging', 'mga', 'reports', 'sa', 'cybercrime', 'sama', 'sa', 'phishing.', 'Gisubli', 'sab', 'sa', 'maong', 'team', 'nga', 'wala', 'gibutang', 'sa', 'website', 'ang', '"', 'About', 'Us', '"', 'page', ',', 'nga', 'magbutyag', 'sa', 'developers', 'niini.', 'Sumala', 'pa', 'sa', 'NCERT', ',', 'liboan', 'na', 'ka', 'mga', 'tawo', 'ang', 'nirehistro', 'sa', 'maong', 'website', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 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, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,416
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagsugod', 'sa', 'pagdagsa', 'ang', 'mga', 'estudyante', 'sa', 'Silliman', 'University', 'sa', 'iconic', 'nga', 'Bossing', 'Tempura', 'stall', 'nga', 'sikat', 'sa', 'ilang', 'five-spice', 'level', 'sauces.', 'Nibalik', 'na', 'sila', 'human', 'sa', 'duha', 'ka', 'tuig', 'nga', 'wala', 'naninda', 'tungod', 'sa', 'pandemya.', 'Abri', 'sila', 'gikan', '10am', 'hangtod', '7pm', 'matag', 'Lunes', 'hangtod', 'Sabado', '.'] 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, 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]
|
cebuaner
|
4,417
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'mga', 'sikat', 'nga', 'artista', 'ang', 'moanhi', 'sa', 'Provincial', 'Oval', ',', 'Kagawasan', 'Freedom', 'Park', 'karong', 'December', '10', ',', '2022', 'alang', 'sa', 'TMFunPasko', 'Music', 'Fest.', 'Moanhi', 'sa', 'dakbayan', 'sila', 'si', 'Alden', 'Richards', ',', 'Andrea', 'Brillantes', ',', 'SB19', ',', 'The', 'Juans', ',', 'Matthaios', ',', 'KAIA', ',', 'Asian', 'Cutie', ',', 'Seth', 'Fedelin', ',', 'Baninay', 'Bautista', 'at', 'Gaiapoly', '.'] 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, 6, 6, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 0, 3, 0, 3, 4, 0, 1, 0, 3, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 0]
|
cebuaner
|
4,418
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P30', 'NGA', 'PLITE', 'HANGTOD', 'NOV.', '30', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'P30', 'one-way', 'base', 'fare', '(', 'exclusive', 'ang', 'fees', 'ug', 'surcharges', ')', 'sugod', '10', 'a.m.', 'sa', 'Nobyembre', '28', 'hangtod', '30', ',', '2022.', 'Kadtong', 'mga', 'gaplano', 'nga', 'mobiyahe', 'sunod', 'tuig', ',', 'mahimo', 'nga', 'magsugod', 'og', 'pag-book', 'alang', 'sa', 'ilang', 'biyahe', 'sa', 'mga', 'lokal', 'sa', 'destinasyon', 'sama', 'sa', 'Davao', ',', 'General', 'Santos', ',', 'Zamboanga', ',', 'o', 'all-time', 'favorite', 'beach', 'destinations', 'sama', 'sa', 'Boracay', ',', 'Cebu', ',', 'Bohol', ',', 'Siargao', ',', 'ug', 'Palawan.', 'Ang', 'travel', 'period', 'sa', 'maong', 'seat', 'sale', 'magsugod', 'sa', 'Hunyo', '1', ',', '2023', 'hangtod', 'Setyembre', '30', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,419
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', 'GIPASIDUNGGAN', 'SA', 'DOH', 'SA', 'KADAGHAN', 'SA', 'NABAKUNAHAN', 'BATOK', 'COVID-19', 'Nakadawat', 'ang', 'Dumaguete', 'City', 'og', 'tulo', 'ka', 'awards', 'atol', 'sa', '2022', 'Local', 'Health', 'Systems', 'Awarding', 'Ceremony', 'sa', 'Central', 'Visayas', 'alang', 'sa', 'Health', 'Development', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'niadtong', 'Nobyembre', '25', ',', '2022', 'sa', 'Cebu', 'City.', 'Gidawat', 'kini', 'ni', 'City', 'Health', 'Officer', 'Dr.', 'Sarah', 'B.', 'Talla', '(', 'isip', 'representante', 'ni', 'Mayor', 'Felipe', 'Antonio', 'B.', 'Remollo', ')', ',', 'DOH', 'Negros', 'Oriental', 'Jennifer', 'Remollo', 'ug', 'CHO', 'Nurse', 'Vanessa', 'Campoy', '.'] 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, 3, 0, 0, 0, 0, 0, 7, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 5, 6, 0, 0, 7, 8, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 3, 4, 4, 1, 2, 0, 3, 0, 1, 2, 0]
|
cebuaner
|
4,420
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'rollback', 'sa', 'presyo', 'sa', 'petrolyo', ',', 'epektibo', 'ugmang', 'adlawa', ',', 'Nobyembre', '29', ',', '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.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,421
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TAXI', 'DRIVER', 'NA', ',', 'SINGER', 'PA', '!', 'Mao', 'kini', 'ang', 'nasaksihan', 'sa', 'usa', 'ka', 'pasahero', 'samtang', 'nagsakay', 'siya', 'ug', 'taxi', 'ngadto', 'sa', 'Sugbo.', 'Wala', 'na', 'batyagan', 'sa', 'pasahero', 'ang', 'kakapoy', 'ug', 'kalangay', 'sa', 'trapik', 'kay', 'nalipay', 'siya', 'pag', 'videoke', 'ni', 'kuya', 'driver', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,422
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mahimong', 'molantaw', 'sa', 'livestream', 'sa', '2022', 'FIFA', 'World', 'Cup', 'sa', 'Pantawan', 'People’s', 'Park', ',', 'Rizal', 'Boulevard.', 'Ang', 'maong', 'kalihukan', ',', 'LIBRE', 'alang', 'sa', 'tanan', '.'] 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, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,423
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SCULPTURE', 'NGA', 'INGON', 'KA', 'GAMAY', 'SA', 'LEAD', 'SA', 'LAPIS', 'Mao', 'kini', 'ang', 'mga', 'hulagway', 'sa', 'mga', '"', 'Miniature', 'Sculpture', '"', 'ni', 'Renz', 'Guevarra', 'Calalo', 'sa', 'Our', 'Lady', 'of', 'Peñafrancia', ',', 'kandila', ',', 'lalaking', 'aduna'y', 'gikuptan', 'nga', 'bola', ',', 'ug', 'uban', 'pa.', 'Ang', 'gigamit', 'niya', 'mao', 'ang', 'pencil', 'lead', 'ug', 'cutter', 'aron', 'himuon', 'ang', 'iyang', 'obra.', 'Sumala', 'pa', 'niya', ',', 'moabot', 'siya', 'og', 'usa', 'ka', 'oras', 'o', 'mubo', 'pa', 'niini', 'sa', 'paggama', 'sa', 'iyang', 'sculpture', '.'] 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, 1, 2, 2, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,424
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gilaoman', 'nga', 'aduna'y', 'rollback', 'sa', 'presyo', 'sa', 'gasolina', 'sa', 'mosunod', 'nga', 'semana', ',', 'sumala', 'pa', 'ni', 'Rodela', 'Romero', ',', 'assistant', 'director', 'sa', 'Department', 'of', 'Energy’s', 'Oil', 'Industry', 'Management', 'Bureau', '.'] 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, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0]
|
cebuaner
|
4,425
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['P33,000', 'NGA', 'NAT'L', 'MINIMUM', 'WAGE', 'ALANG', 'SA', 'GOV'T', 'WORKERS', ',', 'GISUGYOT', 'Usa', 'ka', 'grupo', 'sa', 'mga', 'trabahante', 'ang', 'nanawagan', 'sa', 'gobyerno', 'niadtong', 'Huwebes', 'nga', 'ipasaka', 'ngadto', 'sa', 'P33,000', 'ang', 'national', 'minimum', 'wage.', 'Sumala', 'pa', 'ni', 'Confederation', 'for', 'Unity', ',', 'Recognition', 'and', 'Advancement', 'of', 'Government', 'Employees', '(', 'Courage', ')', 'National', 'President', 'Santiago', 'Dasmarinas', 'Jr.', ',', 'galisod', 'silang', 'iguon', 'ang', 'ilang', 'sweldo', 'tungod', 'sa', 'kamahal', 'sa', 'mga', 'palaliton', ',', 'plite', 'ug', 'bills', 'karon.', 'Kung', 'basehan', 'ang', 'pagtuon', 'sa', 'non-government', 'organization', 'nga', 'IBON', 'Foundation', ',', 'moabot', 'na', 'sa', 'P1,119', 'matag', 'adlaw', 'ang', 'family', 'living', 'wage', 'para', 'sa', 'usa', 'ka', 'pamilya', 'nga', 'aduna'y', 'lima', 'ka', 'miyembro.', 'Apan', 'ang', 'maong', 'kalkulasyon', ',', 'niadto', 'pang', 'Setyembre', 'diin', 'hinay', 'pa', 'ang', 'inflation', 'o', 'pagsaka', 'sa', 'presyo', 'sa', 'mga', 'palaliton', 'ug', 'serbisyo.', 'Sa', 'pagkakaron', ',', 'P12,517', 'ang', 'sweldo', 'sa', 'pinakaubos', 'nga', 'pwesto', 'sa', 'gobyerno', 'ug', 'mas', 'ubos', 'pa', 'sa', 'mga', 'anaa', 'sa', 'lokal', 'nga', 'pamahalaan.', 'Nanawagan', 'sab', 'ang', 'Courage', 'nga', 'himuon', 'ng', 'regular', 'kadtong', 'mga', 'kontraktuwal', 'nga', 'trabahante', '.'] 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 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, 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, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,426
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipagawas', 'sa', 'NASA', 'ang', 'mga', 'close-up', 'shots', 'sa', 'Bulan', 'sa', 'Orion', 'spacecraft', 'isip', 'parte', 'sa', 'ilang', 'Artemis', 'I', 'mission.', 'Makita', 'sa', 'mga', 'litrato', 'ang', 'mga', 'crater', 'ug', 'tekstura', 'sa', 'Bulan', 'tungod', 'sa', 'pagbangga', 'sa', 'mga', 'meteor', 'ug', 'asteroid', '.'] 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, 5, 0, 7, 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]
|
cebuaner
|
4,427
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NAT'L', 'LAW', 'GIDEKLARAR', 'ANG', 'IKA-24', 'SA', 'NOBYEMBRE', 'MATAG', 'TUIG', 'ISIP', 'SPECIAL', 'HOLIDAY', 'SA', 'DUMAGUETE', 'CITY', 'Subay', 'sa', 'Republic', 'Act', 'No.', '7253', ',', 'ang', 'ika-24', 'nga', 'adlaw', 'sa', 'Nobyembre', 'matag', 'tuig', 'gideklarar', 'isip', 'special', 'holiday', 'alang', 'sa', 'Dumaguete', 'City', ',', 'Negros', 'Oriental', 'sa', 'obserbasyon', 'sa', 'Charter', 'Day', 'Anniversary', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 6, 6, 0]
|
cebuaner
|
4,428
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mag-abri', 'og', 'bag-ong', 'museum', 'branch', 'ang', 'National', 'Museum', 'of', 'the', 'Philippines', 'aron', 'ipakita', 'ang', 'kultura', ',', 'kabilin', ',', 'ug', 'kasaysayan', 'sa', 'Negros', 'Oriental', 'ug', 'Siquijor.', 'Ipahigayon', 'ang', 'public', 'opening', 'niini', 'sa', 'The', 'Presidencia', '(', 'the', 'old', 'Dumaguete', 'City', 'Hall', ')', 'karong', 'Biyernes', ',', 'Nobyembre', '25', ',', '2022.', 'Sa', 'maong', 'kalihukan', ',', 'ipahigayon', 'sab', 'ang', 'pagbutyag', 'sa', 'cultural', 'marker', 'alang', 'sa', 'The', 'Presidencia', ',', 'nga', 'gidisenyo', 'ug', 'gihimo', 'sa', 'bantogang', 'Filipino', 'architect', 'nga', 'si', 'Juan', 'Arellano', 'niadtong', '1937', '.'] 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, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 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, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 2, 0, 0, 0]
|
cebuaner
|
4,429
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['USA', 'KA', 'POTENSYAL', 'NGA', 'BAGYO', ',', 'MAHIMONG', 'MOSULOD', 'SA', 'PAR', 'SUNOD', 'SEMANA', ';', 'APAN', 'MAHIMO', 'PANG', 'MAGBAG-O', 'Usa', 'ka', 'potensyal', 'nga', 'bagyo', 'ang', 'mahimong', 'mosulod', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'sa', 'mosunod', 'nga', 'semana', 'ug', 'mahimong', 'maglihok', 'paingon', 'sa', 'Luzon', 'pagsulod', 'sa', 'Disyembre', ',', 'base', 'sa', 'gipakita', 'karon', 'sa', 'GFS', 'weather', 'model.', 'Sa', 'pagkakaron', ',', 'dili', 'pa', 'sigurado', 'kung', 'asa', 'kini', 'insaktong', 'moagi', 'tungod', 'aduna'y', 'mga', 'weather', 'model', 'nga', 'nag-ingon', 'sab', 'nga', 'motabok', 'kini', 'sa', 'Visayas', 'ug', 'sa', 'Mindanao.', 'Padayon', 'kining', 'bantayan', 'sa', 'mga', 'mosunod', 'nga', 'adlaw', 'tungod', 'aduna', 'gihapon', 'posibilidad', 'nga', 'kini', 'MAGBAG-O.', 'Ang', 'mosunod', 'nga', 'pangalan', 'sa', 'bagyo', 'nga', 'gamiton', 'sa', 'PAGASA', 'mao', 'ang', '"', 'ROSAL', '"', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0]
|
cebuaner
|
4,430
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MISS', 'EARTH', 'QUEENS', 'AT', 'THE', 'CAMPUS', 'BY', 'THE', 'SEA', 'LOOK', ':', 'Candidates', 'of', 'the', 'Miss', 'Earth', '2022', 'pageant', 'visit', 'Silliman', 'University', 'on', 'Tuesday', ',', 'Nov.', '22.', 'Fresh', 'from', 'their', 'evening', 'gown', 'competition', 'Monday', 'night', ',', 'the', 'ladies', 'are', 'visiting', 'Silliman', 'among', 'their', 'last', 'stops', 'before', 'leaving', 'Dumaguete', 'City', 'in', 'the', 'afternoon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 4, 0, 5, 6, 6, 6, 6, 0, 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, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0]
|
cebuaner
|
4,431
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GIINGONG', 'TAAS', 'NGA', 'OPISYAL', 'SA', 'NPA', 'SA', 'NEGROS', ',', 'NAPATAY', 'SA', 'PINUSILAY', 'SA', 'GUIHULNGAN', 'Usa', 'ka', 'giingong', 'taas', 'nga', 'opisyal', 'sa', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', 'ang', 'napatay', 'sa', 'engkwentro', 'tali', 'sa', 'kasundaluhan', 'sa', 'Barangay', 'Trinidad', ',', 'Guihulngan', 'City', 'niadtong', 'Lunes', ',', 'Nobyembre', '21', ',', '2022.', 'Sumala', 'pa', 'sa', 'report', ',', 'niadto', 'ang', 'kasundaluhan', 'sa', 'maong', 'dapit', 'human', 'makadawat', 'og', 'reports', 'gikan', 'sa', 'mga', 'lumolupyo', 'bahin', 'sa', 'presensya', 'sa', 'mga', 'armadong', 'mga', 'tawo', ',', 'apan', 'gikatahong', 'gipabuthan', 'sila', 'sa', 'mga', 'rebelde', 'samtang', 'nagkaduol', 'sila.', 'Giila', 'ang', 'napatay', 'nga', 'si', 'Victor', 'Baldonado', ',', 'alias', 'Rudy', ',', 'giingong', 'commanding', 'officer', 'sa', 'Section', 'Guerrilla', 'Unit', '3', '(', 'SGU3', ')', ',', 'Central', 'Negros', 'Front', '1', '(', 'CN1', ')', ',', 'Komiteng', 'Rehiyon', 'Negros', ',', 'Cebu', ',', 'Bohol', ',', 'and', 'Siquijor', '(', 'KR-NCBS', ')', ',', 'nga', 'naglihok', 'sa', 'tri-bounderies', 'sa', 'Guihulngan', 'City', 'ug', 'Canlaon', 'City', 'sa', 'Negros', 'Oriental', 'ug', 'Moises', 'Padilla', 'sa', 'Negros', 'Occidental.', 'Giingong', 'si', 'Baldonado', 'ug', 'ang', 'iyang', 'grupo', 'mao'y', 'responsable', 'sa', 'daghang', 'human', 'rights', 'violations', 'sa', 'isla', 'sa', 'Negros.', 'Narekober', 'sa', 'mga', 'sundalo', 'ang', 'usa', 'ka', 'M-16', 'riffle', 'uban', 'sa', '3', 'ka', 'magazines', 'nga', 'aduna'y', 'kargang', '43', 'rounds', 'sa', 'bala', ',', 'usa', 'ka', 'backpack', 'nga', 'aduna'y', 'sulod', 'nga', 'personal', 'nga', 'mga', 'butang.', 'Gidayeg', 'ni', 'Brig.', 'Gen.', 'Inocencio', 'Pasaporte', ',', '303rd', 'Infantry', 'Brigade', 'commander', ',', 'ang', 'mga', 'residente', 'sa', 'Guihulngan', 'alang', 'sa', 'ilang', 'nagpadayon', 'nga', 'suporta', ',', 'ilabi', 'na', 'sa', 'paghatag', 'og', 'impormasyon', 'sa', 'kasundaluhan', '.'] 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, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 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, 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, 1, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 0, 5, 6, 0, 1, 2, 0, 5, 6, 0, 0, 1, 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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,432
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DOH', ':', 'PH', ',', 'NAUSIK', 'ANG', 'KAPIN', '31-M', 'KA', 'COVID-19', 'VACCINE', 'DOSES', 'NGA', 'NAGKANTIDAD', 'OG', 'P15.6-B', 'Kapin', '31', 'milyon', 'shots', 'sa', 'Covid-19', 'vaccines', 'nga', 'nagkantidad', 'og', 'P15.6', 'bilyon', 'ang', 'nausik', 'sa', 'Pilipinas', ',', 'matod', 'pa', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'niadtong', 'Lunes', ',', 'Nobyembre', '21', ',', '2022.', 'Sumala', 'pa', 'ni', 'DOH', 'officer-in-charge', 'Maria', 'Rosario', 'Vergeire', ',', '24', 'milyon', 'sa', 'mga', 'nausik', 'nga', 'doses', 'ang', 'na-expired', 'tungod', 'sa', 'short', 'shelf', 'life.', 'Dugang', 'pa', 'niya', ',', 'nausik', 'ang', 'nabiling', '7', 'milyon', 'shots', 'sa', 'Covid-19', 'vaccines', 'tungod', 'sa', 'temperature', 'excursion', ',', 'samtang', 'abri', 'ug', 'wala', 'nagamit', 'ang', 'ubang', 'vials.', 'Sa', '31', 'milyon', ',', 'dul-an', '70', '%', 'niini', 'ang', 'napalit', 'sa', 'private', 'sector', 'ug', 'local', 'government.', 'Gisubli', 'sa', 'health', 'officials', 'nga', 'ang', 'nauna', 'nga', 'mga', 'bakuna', 'sa', 'Covid-19', ',', 'aduna', 'lamang', 'shelf', 'life', 'nga', '6', 'months', 'gikan', 'sa', 'date', 'of', 'bottling.', 'Sa', 'usa', 'ka', 'interview', ',', 'gisubli', 'sab', 'ni', 'Vergeire', 'nga', 'magpahigayon', 'ang', 'DOH', 'og', '2-day', 'special', 'COVID-19', 'vaccination', 'drive', 'sa', 'Disyembre.', 'Tawgon', 'kining', '"', 'Bakunahang', 'Bayan', 'II', ',', '"', 'ang', 'special', 'vaccination', 'days', 'anaa', 'sa', 'Disyembre', '5-7', 'ug', 'ang', 'target', 'mao', 'ang', 'mga', 'bata', 'nga', 'nag-edad', 'og', '5-11', 'anyos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 5, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 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, 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, 7, 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, 3, 0, 0, 0, 7, 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]
|
cebuaner
|
4,433
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SU', 'NIDEKLARAR', 'OG', 'CLIMATE', 'EMERGENCY', ';', 'PANAWAGAN', 'SA', 'DINALIANG', 'AKSYON', 'ALANG', 'SA', 'CLIMATE', 'CHANGE', 'Nagdeklarar', 'ang', 'Silliman', 'University', '(', 'SU', ')', 'og', 'climate', 'emergency', 'isip', 'panawagan', 'alang', 'sa', 'dinalian', 'nga', 'aksyon', 'aron', 'mapakunhod', 'ang', 'mga', 'kalihukan', 'sa', 'katawhan', 'nga', 'mahimong', 'hinungdan', 'sa', 'climate', 'change', 'ug', 'aron', 'mapalig-on', 'ang', 'mga', 'palisiya', 'sa', 'pagprotektar', 'sa', 'kinaiyahan.', 'Sumala', 'pa', 'ni', 'SU', 'President', 'Dr.', 'Betty', 'Cernol', 'McCann', ',', 'ang', 'deklarasyon', 'nagsilbi', 'nga', 'usa', 'ka', 'organizing', 'framework', 'alang', 'sa', 'tanang', 'adbokasiya', 'sa', 'kinaiyahan', 'sa', 'unibersidad.', 'Lakip', 'na', 'niini', 'ang', 'paggamit', 'og', 'renewable', 'energy', ',', 'on-campus', 'waste', 'management', ',', 'pagtanom', 'sa', 'indigenous', 'tree', 'species', ',', 'wildlife', 'protection', ',', 'preservation', 'ug', 'restoration', 'sa', 'marine', 'habitats', ',', 'ug', 'uban', 'pang', 'environmental', 'activities', 'nga', 'giplano', 'sulod', 'sa', 'mga', 'classrooms', 'ug', 'himuon', 'sa', 'ubay-ubay', 'nga', 'mga', 'komunidad.', 'Sa', 'pagdeklarar', ',', 'ang', 'SU', '"', 'acknowledges', 'that', 'the', 'science', 'of', 'climate', 'change', 'is', 'well-established', ',', 'that', 'climate', 'change', 'is', 'real', ',', 'that', 'its', 'main', 'cause', 'is', 'human', 'activities', 'especially', 'the', 'burning', 'of', 'fossil', 'fuel', ',', 'and', 'that', 'its', 'consequences', 'are', 'grave.', '"', 'Sumala', 'sab', 'ni', 'Dr.', 'Angel', 'C.', 'Alcala', ',', 'National', 'Scientist', 'ug', 'vice', 'chair', 'sa', 'SU', 'Board', 'of', 'Trustees', ',', 'nga', 'kombinsido', 'ang', 'mga', 'siyentipiko', 'nga', 'ang', '"', 'destructive', 'effects', '"', 'sa', 'climate', 'change', 'tungod', 'sa', 'pagsunog', 'og', 'coal', 'ug', 'fossil', 'fuels', 'aron', 'makahimo', 'og', 'elektrisidad', '.'] 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, 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, 3, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,434
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'telebisyon', ',', 'aduna'y', 'importanteng', 'papel', 'sa', 'pagpakaylap', 'og', 'mga', 'importante', 'ug', 'tinuod', 'nga', 'impormasyon', 'ngadto', 'sa', 'matag', 'panimalay.', 'Karong', 'adlawa', ',', 'Nobyembre', '21', ',', 'atong', 'gisaulog', 'ang', '#', 'WorldTelevisionDay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,435
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SANDUROT', 'FESTIVAL', ',', 'PLANO', 'NGA', 'MOSALMOT', 'SA', 'SINULOG', 'FESTIVAL', '2023', 'Gipaambit', 'ni', 'Mayor', 'Felipe', 'Remollo', 'nga', 'nakigsulti', 'siya', 'ni', 'Cebu', 'City', 'Mayor', 'Michael', 'L.', 'Rama', 'bahin', 'sa', 'iyang', 'plano', 'nga', 'ipadala', 'ang', 'Sandurot', 'contingent', 'aron', 'mosalmot', 'sa', '2023', 'Sinulog', 'Festival.', 'Ang', 'Dumaguete', 'City', 'mao', 'ang', 'Buglasan', 'Festival', 'of', 'Festivals', 'Champion', 'karong', 'tuiga', 'human', 'makadaog', 'ang', 'mga', 'representante', 'niini', 'og', 'pipila', 'ka', 'major', 'competitions', ',', 'partikular', 'na', 'ang', 'Sandurot', 'contingent', 'alang', 'sa', 'Best', 'in', 'Showdown', ',', 'Best', 'in', 'Street', 'Dance', ',', 'Best', 'in', 'ID', 'Arc', 'ug', 'Best', 'in', 'Choreography.', 'Mao', 'kini', 'ang', 'ikaduhang', 'higayon', 'nga', 'mosalmot', 'ang', 'Dumaguete', 'City', 'sa', 'Sinulog', 'Festival.', 'Nakigkompetensya', 'kini', 'niadtong', '2017', 'ug', 'nakadaog', 'sa', 'Free', 'Interpretation', '(', '3rd', 'Place', ')', ',', 'Street', 'Parade', '(', '5th', 'Place', ')', ',', 'Best', 'in', 'Musicality', '(', '5th', 'Place', ')', ',', 'ug', 'Best', 'Choreographer', '(', '3rd', 'Place', ')', '.', 'Bag-ohay', 'lamang', ',', 'nagkatapok', 'ang', 'mga', 'representante', 'sa', 'Dumaguete', 'City', 'ug', 'miyembro', 'sa', 'Sandurot', 'contingent', 'ngadto', 'sa', 'pinuy-anan', 'ni', 'Mayor', 'Remollo', 'alang', 'sa', 'usa', 'ka', 'tradisyonal', 'nga', 'thanksgiving', 'dinner', 'aron', 'isaulog', 'ang', 'ilang', 'kadaugan.', 'Gisubli', 'sab', 'ni', 'Mayor', 'Remollo', 'nga', 'ang', 'ilang', 'kalampusan', 'nagdala', 'og', 'dungog', 'ug', 'garbo', 'sa', 'dakbayan', 'ug', 'angayan', 'sa', 'padayon', 'nga', 'suporta', 'gikan', 'sa', 'local', 'government', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 8, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 8, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 7, 0, 0, 0, 0, 0, 0, 1, 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]
|
cebuaner
|
4,436
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BINILANGGO', 'SA', 'BACONG', 'PNP', ',', 'NAKAESKAPO', 'Usa', 'ka', 'Person', 'Under', 'Police', 'Custody', '(', 'PUPC', ')', 'sa', 'Bacong', 'PNP', 'ang', 'nakaeskapo', 'gikan', 'sa', 'iyang', 'detention', 'cell', 'mga', '3:30', 'sa', 'kadlawon', 'niadtong', 'Miyerkules', ',', 'November', '16', ',', '2022.', 'Giila', 'ang', 'miikyas', 'nga', 'si', 'John', 'Mike', 'Olitres', 'alias', 'Tata', 'Vendetta', ',', '34-anyos', ',', 'usa', 'ka', 'construction', 'worker', ',', 'ug', 'residente', 'sa', 'Purok', 'Sangra', ',', 'Barangay', 'Sacsac', ',', 'Bacong.', 'Sumala', 'pa', 'sa', 'kapulisan', ',', 'wala', 'matarong', 'og', 'kandado', 'ang', 'detention', 'cell', 'kung', 'asa', 'nahimutang', 'si', 'Olitres', 'hinungdan', 'nga', 'nakaeskapo', 'kini.', 'Nisikop', 'kaniadto', 'si', 'Olitres', 'pinaagi', 'sa', 'usa', 'ka', 'buy', 'bust', 'operation', 'sa', 'Barangay', 'Sacsac', ',', 'Bacong', 'niadtong', 'Oktubre', '8', ',', '2022', 'ug', 'nag-atubang', 'sa', 'kaso', 'nga', 'paglapas', 'sa', 'Section', '5', 'sa', 'Article', 'II', 'of', 'R.A.', '9165', 'o', 'mas', 'giila', 'nga', 'Dangerous', 'Drugs', 'Act.', 'Gi-alerto', 'ni', 'Bacong', 'Police', 'Chief', 'Fortunato', 'Villafuerte', 'ang', 'publiko', 'bahin', 'sa', 'pag-ikyas', 'sa', 'usa', 'ka', 'PUPC.', 'Nihangyo', 'sab', 'siya', 'nga', 'kung', 'kinsa', 'ang', 'makakita', 'ni', 'Olitres', ',', 'i-report', 'dayon', 'sa', 'pinakaduol', 'nga', 'police', 'station.', 'Gihangyo', 'sab', 'ni', 'Villafuerte', 'ang', 'nieskapo', 'nga', 'si', 'Olitres', 'nga', 'mu-surrender', 'nalang', 'alang', 'sa', 'iyang', 'seuridad', 'ug', 'alang', 'sa', 'iyang', 'pamilya.', 'Sa', 'pagkakaron', ',', 'nagpahigayon', 'og', 'man-hunt', 'operation', 'ang', 'kapulisan', 'aron', 'masikop', 'pagbalik', 'si', 'Olitres', '.'] 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, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 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, 1, 0]
|
cebuaner
|
4,437
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', 'NAA', 'PA', 'KA'Y', 'CHANCE', 'NGA', 'MAHIMONG', 'MILYONARYO', '!', 'Wala', 'pa', 'gihapon', 'naka-jackpot', 'sa', 'kapin', 'P230', 'milyon', 'nga', 'premyo', 'sa', 'ultra', 'lotto', '6', '/', '58', '.'] 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, 7, 8, 8, 8, 8, 0]
|
cebuaner
|
4,438
|
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', 'mas', 'gipalambo', 'nga', 'kopya', 'sa', 'karaang', 'mapa', 'sa', 'Isla', 'sa', 'Negros', ',', 'base', 'sa', '"', 'Carta', 'Hydrographica', 'y', 'Chorographica', 'de', 'las', 'Islas', 'Philippine', '"', '(', 'Murillo', 'Velarde', 'map', ')', 'nga', 'gihimo', 'sa', 'Spanish', 'cartographer', 'nga', 'si', 'Pedro', 'Murillo', 'Velarde', 'ug', 'sa', 'tabang', 'sa', 'duha', 'ka', 'Pilipino', 'niadtong', '1734', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 7, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0]
|
cebuaner
|
4,439
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Anaa', 'na', 'sa', 'Pilipinas', 'ang', 'streaming', 'platform', 'nga', 'Disney+', ',', 'nga', 'may', 'library', 'sa', 'mga', 'classic', 'Disney', 'films', ',', 'series', ',', 'ug', 'uban', 'pang', 'original', 'content.', 'Giawhag', 'sa', 'Disney', 'ang', 'mga', 'fans', 'niini', 'nga', 'isaulog', 'ang', '"', 'A', 'Night', 'of', 'Wonder', '"', 'sa', 'pareha', 'nga', 'adlaw', 'diin', 'opisyal', 'nga', 'ilusad', 'ang', 'Disney+', 'sa', 'nasud.', 'Pipila', 'ka', 'mga', 'Filipino', 'personalities', 'ug', 'musical', 'acts', 'ang', 'mag-uban', 'alang', 'sa', 'usa', 'ka', 'special', 'Disney+', 'livestream', '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, 5, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 3, 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, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0]
|
cebuaner
|
4,440
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gilusad', 'sa', 'NASA', ''s', 'Space', 'Launch', 'System', 'rocket', 'ug', 'NASA', ''s', 'Orion', 'Spacecraft', 'ang', '#', 'Artemis', '1', 'test', 'flight', 'paingon', 'sa', 'Moon', 'gikan', 'sa', 'Kennedy', ''s', 'Launch', 'Complex', '39B', 'niadtong', 'Miyerkules', ',', 'Novermeber', '16', ',', '2022', ',', 'sa', '1:47am', 'ES'] 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, 7, 8, 8, 8, 0, 0, 7, 8, 0, 0, 0, 0, 5, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,441
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TRUMP', 'MOLANSAR', 'NA', 'SAB', 'PAGKA-PRESIDENTE', 'SA', 'AMERIKA', 'SA', '2024', 'Gilusad', 'ni', 'Donald', 'Trump', 'ang', 'iyang', 'presidential', 'bid', 'alang', 'sa', 'tuig', '2024', 'niadtong', 'Martes', ',', 'ug', 'tumong', 'nga', 'mapunggan', 'ang', 'iyang', 'potensyal', 'nga', 'mga', 'Republican', 'rivals.', 'Gianunsyo', 'kini', 'ni', 'Trump', 'sa', 'iyang', 'Mar-a-Lago', 'estate', 'sa', 'Florida', 'usa', 'ka', 'semana', 'human', 'ang', 'midterm', 'elections', 'diin', 'napakyas', 'ang', 'mga', 'Republicans', 'nga', 'makadaog', 'og', 'daghang', 'lingkuranan', 'sa', 'Kongreso', 'sama', 'sa', 'ilang', 'gilaoman.', 'Sa', 'iyang', 'speech', 'nga', 'nilungtad', 'og', 'kapin', 'usa', 'ka', 'oras', ',', 'nakig-storya', 'siya', 'sa', 'iyang', 'gatusan', 'ka', 'mga', 'tagasuporta', 'sa', 'usa', 'ka', 'ballroom', 'nga', 'giadornohan', 'og', 'chandeliers', 'ug', 'galinya', 'nga', 'mga', 'American', 'flags', '.'] 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, 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, 3, 0, 0, 0, 0, 1, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 7, 0, 0]
|
cebuaner
|
4,442
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-isyu', 'si', 'President', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'Proclamation', 'No.', '90', ',', 'nga', 'nagdeklarar', 'sa', 'mga', 'regular', 'holidays', 'ug', 'special', '(', 'non-working', ')', 'days', 'alang', 'sa', '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, 1, 2, 2, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,443
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mahitabo', 'ang', '''', 'Harry', 'Styles', ':', 'Love', 'On', 'Tour', ''', 'sa', 'Pilipinas', 'karong', 'Marso', '14', ',', '2023', 'sa', 'Philippine', 'Arena.', 'Magsugod', 'ang', 'pre-selling', 'karong', 'Nobyembre', '23', ',', '2022', 'pagka', '12:00pm', '.'] 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, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,444
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LA', 'LIBERTAD', 'NANGHATAG', 'OG', 'LIBRE', 'NGA', 'PANIUDTO', 'SA', 'TANANG', 'ESTUDYANTE', 'SA', 'KINDERGARTEN', ',', 'ELEMENTARYA', 'Nanghatag', 'og', 'libre', 'nga', 'paniudto', 'alang', 'sa', 'tanang', 'estudyante', 'sa', 'kindergarten', 'ug', 'elementarya', 'ang', 'Kagamhanan', 'sa', 'Lungsod', 'sa', 'La', 'Libertad', 'pinaagi', 'ni', 'Mayor', 'Emmanuel', '"', 'MM', '"', 'Limkaichong', 'Iway', 'uban', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', '.', 'Sumala', 'pa', 'ni', 'Dr.', 'Adela', 'Araula', ',', 'kinsa', 'DepEd', 'La', 'Libertad', 'District', '1', 'Supervisor', ',', 'ilalom', 'sa', 'memorandum', 'sa', 'DepEd', 'ang', 'maong', 'programa', 'alang', 'sa', 'mga', 'bata', 'nga', '"', 'severe', 'malnourished', 'and', 'malnourished', '"', 'base', 'sa', 'ilang', 'datos.', 'Nigahin', 'og', 'pondo', 'ang', 'LGU', 'sa', 'maong', 'lungsod', 'aron', 'maapil', 'sa', 'libreng', 'paniudto', 'matag', 'adlaw', 'ang', 'tanang', 'estudyante', 'sa', 'kindergarten', 'ug', 'elementarya', ',', 'bisan', 'paman', 'wala', 'sila', 'naklasipikar', 'nga', ''malnourished'.', 'Gisugdan', 'sa', 'DepEd', 'ang', 'feeding', 'program', 'niadtong', 'Enero', 'sa', 'dihang', 'nagsugod', 'sab', 'ang', 'limitado', 'nga', 'face-to-face', 'classes.', 'Samtang', ',', 'gisugdan', 'sa', 'LGU', 'sa', 'La', 'Libertad', 'ang', 'susamang', 'programa', 'niadtong', 'Nobyembre', '2', 'atol', 'sa', 'pagsugod', 'sa', 'full', 'in-person', 'classes.', 'Gisubli', 'ni', 'Dr.', 'Araula', 'nga', 'dakong', 'tabang', 'ang', 'inisyatibo', 'ni', 'Mayor', 'Iway', 'alang', 'sa', 'mga', 'ginikanan', 'ug', 'estudyante', 'tungod', 'makamenos', 'ang', 'mga', 'ginikanan', 'og', 'galastuhan', 'sa', 'pagkaon', 'sa', 'ilang', 'anak', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 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, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,445
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['5-ANYOS', 'NGA', 'BATANG', 'BABAYI', 'KINSA', 'ANAK', 'SA', 'USA', 'KA', 'NEGRENSE', ',', 'NAKADAOG', 'SA', 'JIU-JITSU', 'WORLD', 'CHAMPIONSHIP', 'Usa', 'ka', '5-anyos', 'nga', 'batang', 'babayi', ',', 'kinsa', 'ang', 'ginikanan', 'taga', 'Negros', 'Occidental', ',', 'ang', 'giila', 'isip', ''youngest', 'Jiu-Jitsu', 'world', 'champion.', ''', 'Nakadaog', 'si', 'Aleia', 'Aielle', 'Aguilar', 'og', 'gold', 'medal', 'sa', 'Kids', '1', 'Girls', 'White', 'Belt', '16kg-B', 'catergory', 'sa', 'Abu', 'Dhabi', 'World', 'Jiu-Jitsu', 'Festival', 'niadtong', 'Biyernes', ',', 'Nobyembre', '11', ',', '2022.', 'Sumala', 'pa', 'sa', 'iyang', 'apohan', 'nga', 'si', 'Art', 'Aguilar', 'niadtong', 'Dominggo', ',', 'Nobyembre', '13', ',', '2022.', 'Iyang', 'gipildi', 'si', 'Gabriela', 'Vercosa', 'sa', 'Brazil', 'sa', 'finals', 'match.', 'Anak', 'siya', 'ni', 'Alvin', 'Aguilar', ',', 'founder', 'sa', 'Philippine', 'Mixed', 'Martial', 'Art', 'ug', 'Asian', 'Champion', 'sa', 'Tokyo.', 'Gipanganak', 'siya', 'sa', 'Bacolod', 'City', 'ug', 'usa', 'ka', 'Freemason', 'sa', 'Negros.', 'Ang', 'iyang', 'inahan', 'mao', 'si', 'May', 'Masuda', ',', 'unang', 'Pilipina', 'nga', 'nakadaog', 'sa', 'World', 'Jiu-Jitsu', 'Championship', 'niadtong', '2009', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 1, 2, 2, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 7, 8, 8, 8, 0, 7, 8, 0, 5, 0, 0, 0, 5, 6, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 7, 8, 8, 0, 0, 0]
|
cebuaner
|
4,446
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['WALA', 'PA'Y', 'NAKADAOG.', 'Wala', 'pa'y', 'nakadaog', 'sa', 'kapin', 'P214', 'milyon', 'nga', 'jackpot', 'prize', 'sa', 'Ultra', 'Lotto', '6', '/', '58', 'draw', 'niadtong', 'Biyernes', 'sa', 'gabii', ',', 'Nobyembre', '12', ',', '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.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,447
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P1', 'NGA', 'PLETE', 'SA', 'ILANG', ''11.11', ''', 'PROMO', 'Nagtanyag', 'pag-usab', 'ang', 'Cebu', 'Pacific', 'sa', 'ilang', 'trademark', 'nga', 'P1', 'seat', 'sale', 'sugod', 'Nobyembre', '11', ',', 'mao', 'kini', 'ang', 'gianunsyo', 'sa', 'airline', 'niadtong', 'Huwebes', ',', 'Nobyembre', '10', ',', '2022.', 'Sumala', 'pa', 'sa', 'low', 'cost', 'carrier', ',', 'mahimong', 'maka-avail', 'ang', 'mga', 'biyahero', 'sa', 'promo', 'fare', 'sugod', 'sa', 'tungang', 'gabii', 'sa', 'Nobyembre', '11', 'hangtod', 'Nobyembre', '15.', 'Exclusive', 'ang', 'fees', 'ug', 'surcharges', 'sa', 'P1', 'nga', 'one-way', 'base', 'fare', ',', 'ug', 'aduna'y', 'travel', 'period', 'gikan', 'sa', 'Pebrero', '1', 'hangtod', 'Oktubre', '31', ',', '2023.', 'Modugang', 'sab', 'ang', 'Cebu', 'Pacific', 'og', 'direct', 'flights', 'ngadto', 'sa', 'Bali', 'ug', 'Jakarta', 'sa', 'Indonesia', ';', 'Hanoi', 'ug', 'Ho', 'Chi', 'Minh', 'sa', 'Vietnam', ';', 'Bangkok', 'sa', 'Thailand', ';', 'Kuala', 'Lumpur', 'ug', 'Kota', 'Kinabalu', 'sa', 'Malaysia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 5, 6, 6, 0, 5, 0, 5, 6, 6, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 6, 0, 5, 0]
|
cebuaner
|
4,448
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['EKONOMIYA', 'SA', 'PILIPINAS', 'PASPAS', 'NGA', 'NISAKA', 'NGADTO', 'SA', '7.6', '%', 'SA', 'IKATULONG', 'KWARTER', 'SA', '2022', 'Paspas', 'nga', 'nisaka', 'ang', 'ekonomiya', 'sa', 'Pilipinas', 'sa', 'ikatulo', 'nga', 'kwarter', 'ning', 'tuiga', ',', 'mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'niadtong', 'Huwebes', ',', 'Nobyembre', '10', ',', '2022.', 'Nisaka', 'kini', 'ngadto', 'sa', '7.6', '%', 'sa', 'panahon', 'sa', 'Hulyo', 'hangtod', 'sa', 'Setyembre', ',', 'mas', 'paspas', 'kini', 'nga', 'niusbaw', 'kung', 'itandi', 'sa', '7.5', '%', 'nga', 'GDP', 'niadtong', 'ikaduha', 'nga', 'kwarter', 'sa', '2022.', 'Ang', 'ekonomiya', ',', 'gisukod', 'pinaagi', 'sa', 'gross', 'domestic', 'product', '(', 'GDP', ')', '.', 'Mao', 'kini', 'ang', 'total', 'value', 'sa', 'goods', 'ug', 'services', 'nga', 'nahimo', 'sulod', 'sa', 'espesipiko', 'nga', 'panahon.', 'Mas', 'taas', 'sab', 'kini', 'og', '7', '%', 'GDP', 'kung', 'itandi', 'sa', 'ikatulong', 'kwarter', 'sa', 'niaging', 'tuig', '.'] 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, 5, 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, 0, 0, 0, 0]
|
cebuaner
|
4,449
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', 'GIILA', 'NGA', 'IKA-3', 'SA', 'RANKING', 'SA', 'MGA', 'SYUDAD', 'SA', 'CENTRAL', 'VISAYAS', 'NGA', 'ADUNAY', 'PINAKATAAS', 'NGA', 'IHAP', 'SA', 'COVID-19', 'SAFETY', 'SEALS', 'SA', 'MGA', 'ESTABLISEMENTO', 'Giila', 'ang', 'dakbayan', 'sa', 'Dumaguete', 'nga', 'ika-3', 'sa', 'ranking', 'sa', 'mga', 'independent', 'component', 'cities', 'nga', 'aduna'y', 'pinakataas', 'nga', 'ihap', 'sa', 'gi-isyu', 'nga', 'mga', 'Safety', 'Seals', 'ngadto', 'sa', 'mga', 'rehistradong', 'establisemento', 'sulod', 'sa', 'hurisdiksyon', 'sa', 'tibuok', 'rehiyon', 'sa', 'Central', 'Visayas.', 'Gihatag', 'ang', 'pag-ila', 'pinaagi', 'sa', 'Department', 'of', 'Interior', 'and', 'Local', 'Government', '(', 'DILG', ')', 'ug', 'nagpamatuod', 'sa', 'malampuson', 'nga', 'pagtubag', 'sa', 'dakbayan', 'batok', 'sa', 'pandemya', 'sa', 'Covid-19.', 'Gidawat', 'ni', 'City', 'Health', 'Officer', 'Dr.', 'Maria', 'Sarah', 'B.', 'Talla', ',', 'kinsa', 'nagrepresentar', 'ni', 'Mayor', 'Felipe', 'Antonio', 'Remollo', ',', 'ang', 'award', 'gikan', 'ni', 'DILG', 'Regional', 'Director', 'Leocadio', 'Trovela', 'uban', 'ni', 'Negros', 'Oriental', 'Provincial', 'Director', 'Farah', 'Diba', 'Gentuya', 'ug', 'CLGOO', 'Anson', 'Baroro.', 'Atol', 'kini', 'sa', '2022', 'Regional', 'POC', 'and', 'ADAC', 'Performance', 'Audit', 'and', 'Safety', 'Seal', 'Awarding', 'Ceremony', 'nga', 'gipahigayon', 'sa', 'Cebu', 'City.', 'Ang', 'establisemento', 'nga', 'aduna'y', 'Safety', 'Seal', 'mao'y', 'pruweba', 'nga', 'nagsunod', 'kini', 'sa', 'minimum', 'public', 'health', 'standards', 'nga', 'gitakda', 'sa', 'gobyerno', 'ug', 'paggamit', 'sa', 'digital', 'contact', 'tracing', 'sa', 'StaySafe.ph.', 'application', 'apil', 'na', 'ang', 'pagdugang', 'sa', 'pagsalig', 'sa', 'mga', 'konsumidor', 'aron', 'luwas', 'nga', 'mabuksan', 'pag-usab', 'ang', 'ekonomiya', '.'] 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, 5, 6, 0, 0, 0, 0, 0, 0, 7, 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, 5, 6, 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, 7, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 5, 6, 0, 0, 1, 2, 2, 0, 3, 1, 2, 0, 0, 0, 0, 3, 4, 0, 7, 8, 8, 8, 8, 8, 8, 8, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,450
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nalakip', 'ang', 'Silliman', 'University', 'sa', '16', 'ka', 'mga', 'unibersidad', 'sa', 'Pilipinas', 'sa', '2023', 'Quacquarelli', 'Symonds', '(', 'QS', ')', 'Asia', 'University', 'Rankings', '.'] 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, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0]
|
cebuaner
|
4,451
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', ',', 'Nobyembre', '8', ',', 'mao', 'ang', 'ika-9', 'nga', 'anibersaryo', 'sa', 'Super', 'Typhoon', 'Yolanda', ',', 'usa', 'sa', 'pinakamakamatay', 'nga', 'natural', 'disaster', 'sa', 'kalibutan.', 'Gikusokuso', 'sa', 'Bagyong', 'Yolanda', 'ang', 'pipila', 'ka', 'mga', 'lalawigan', 'sa', 'Pilipinas', ',', 'diin', 'ang', 'Leyte', 'ug', 'Samar', 'mao', 'ang', 'nakahiagom', 'sa', 'labing', 'kadaot', 'niini.', 'Kapin', '6,000', 'ka', 'mga', 'tawo', 'ang', 'namatay', 'tungod', 'sa', 'maong', 'super', 'typhoon', '(', 'international', 'name', ':', 'Haiyan', ')', ',', 'tungod', 'gipapas', 'niini', 'ang', 'tanan', 'niya', 'nga', 'maagian', 'samtang', 'nagbanlas', 'kini', 'sa', 'baybayon', 'niadtong', 'Nobyembre', '8', ',', '2013', '.'] 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,452
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UNEMPLOYMENT', 'NIUS-OS', 'SA', '5', '%', ',', 'APAN', 'UNDEREMPLOYMENT', 'NISAKA', 'SA', '15.4', '%', 'NIADTONG', 'SETYEMBRE', 'Nius-os', 'ang', 'unemployment', 'rate', 'sa', 'Pilipinas', 'niadtong', 'Setyembre', 'apan', 'nisaka', 'ang', 'underemployment', 'rate', ',', 'matod', 'pa', 'sa', 'datos', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'niadtong', 'Martes', ',', 'Nobyembre', '8', ',', '2022.', 'Anaa', 'sa', '5', '%', 'ang', 'unemployment', 'rate', 'niadtong', 'Setyembre', ',', 'katumbas', 'sa', 'dul-an', '2.5', 'milyon', 'nga', 'mga', 'trabahante.', 'Mas', 'ubos', 'kini', 'kung', 'itandi', 'sa', '5.3', '%', 'rate', 'sa', 'mga', 'wala'y', 'trabaho', 'niadtong', 'Agosto', ',', 'katumbas', 'sa', '2.68', 'milyon', 'nga', 'mga', 'trabahante.', 'Sumala', 'pa', 'sa', 'PSA', ',', 'ang', 'jobless', 'rate', 'niadtong', 'Setyembre', 'mao', 'ang', 'pinakamubo', 'nga', 'rate', 'sukad', 'pa', 'niadtong', 'Oktubre', '2019.', 'Sa', 'laing', 'bahin', ',', 'nisaka', 'ang', 'underemployment', 'rate', 'niadtong', 'Setyembre', 'ngadto', 'sa', '15.4', '%', ',', 'katumbas', 'sa', '7.33', 'milyon', 'nga', 'mga', 'trabahante', 'nga', 'nangita', 'og', 'dugang', 'nga', 'mga', 'trabaho', 'o', 'oras', 'sa', 'trabaho.', 'Mas', 'taas', 'kini', 'kung', 'itandi', 'sa', '14.7', '%', 'nga', 'underemployment', 'rate', 'o', '7.03', 'milyon', 'nga', 'underemployed', 'workers', 'niadtong', 'Agosto', '2021.', 'Hinuon', ',', 'gisubli', 'sa', 'PSA', 'nga', 'niusbaw', 'og', 'gamay', 'ang', 'kinatibuk-ang', 'employment', 'rate', 'niadtong', 'Setyembre', 'kung', 'itandi', 'sa', 'niaging', 'bulan.', 'Ang', 'maong', 'bulan', 'sab', 'ang', 'nakatala', 'og', 'pinakataas', 'nga', 'employment', 'rate', 'sukad', 'pa', 'niadtong', 'Enero', '2020', '.'] 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, 5, 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, 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, 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]
|
cebuaner
|
4,453
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', ',', 'Nobyembre', '8', ',', '2022', ',', 'aduna'y', 'total', 'lunar', 'eclipse', 'ug', 'makita', 'kini', 'sa', 'Pilipinas', 'gikan', '5:19pm', 'hangtod', '9:58pm', ',', 'sumala', 'pa', 'sa', 'state', 'meteorologist', '.'] 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,454
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PNP', 'NAGPASIDAAN', 'SA', '"', 'IKAW', 'BA', ''TONG', 'NASA', 'VIDEO', '?', '"', 'SCHEME', 'Kinahanglang', 'likayan', 'sa', 'mga', 'Pilipino', 'sa', 'social', 'media', 'ang', 'clickbait', 'links', ',', 'ilabi', 'na', 'kadtong', 'mga', 'manguta', ',', '"', 'Ikaw', 'ba', ''tong', 'nasa', 'video', '?', '"', 'aron', 'malikayan', 'sab', 'ang', 'posibleng', 'phishing', 'scheme.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'niadtong', 'Lunes', ',', 'Nobyembre', '7', ',', '2022.', 'Gisubli', 'sa', 'tigpamaba', 'sa', 'PNP', 'nga', 'si', 'Col.', 'Jean', 'Fajardo', ',', 'dili', 'dapat', 'i-click', 'ang', 'link', 'gikan', 'sa', 'usa', 'ka', 'mensahe', 'nga', 'gipadala', 'sa', 'tawo', 'nga', 'wala', 'ka', 'kaila', 'ug', 'nagdani', 'nimo', 'nga', 'motan-aw', 'og', 'video.', 'Sumala', 'pa', 'niya', ',', 'kung', 'ma-click', 'ang', 'link', 'mabalhin', 'ang', 'user', 'ngadto', 'sa', 'laing', 'page', 'o', 'account.', 'Mao', 'kini', 'ang', 'mahimong', 'hinungdan', 'nga', 'ma-access', 'ang', 'imong', 'personal', 'nga', 'mga', 'account', ',', 'lakip', 'na', 'ang', 'GCash', 'accounts.', 'Dugang', 'pa', 'ni', 'Fajardo', ',', 'gipanan-aw', 'sa', 'PNP', 'Anti-Cybercrime', 'group', 'ang', 'posibilidad', 'nga', 'aduna'y', 'grupo', 'nga', 'nagpaluyo', 'ani', 'nga', 'scheme.', 'Apan', 'giangkon', 'niya', 'nga', 'nag-atubang', 'sab', 'og', 'mga', 'hagit', 'ang', 'cybercrime', 'unit', 'sa', 'kapolisan', 'bahin', 'niini.', 'Bisan', 'paman', ',', 'nipasalig', 'ang', 'PNP', 'sa', 'publiko', 'nga', 'padayong', 'gisubay', 'sa', 'mga', 'imbestigador', 'ang', 'mga', 'tawo', 'nga', 'nagpaluyo', 'sa', 'maong', 'scheme', '.'] 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 7, 0, 0, 0, 0, 1, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,455
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MISS', 'UNIVERSE', 'PHILIPPINES', 'GIBUKSAN', 'NA', 'ANG', 'APLIKASYON', 'ALANG', 'SA', '2023', 'PAGEANT', 'NIINI', 'Opisyal', 'nga', 'gibuksan', 'sa', 'organisasyon', 'sa', 'Miss', 'Universe', 'Philippines', 'ang', 'proseso', 'sa', 'aplikasyon', 'niini', 'alang', 'sa', 'Miss', 'Universe', 'Philippines', '2023.', 'Sa', 'Instagram', 'page', 'sa', 'maong', 'organisasyon', ',', 'ni-issue', 'na', 'sila', 'og', 'call', 'out', 'ug', 'nihatag', 'og', 'mga', 'instruksyon', 'niadtong', 'Lunes.', 'Mahimo', 'ng', 'mo-apply', 'aron', 'makaapil', 'sa', 'pageant', 'ang', 'mga', 'babayi', 'bisan', 'unsa', 'paman', 'ang', 'ilang', 'civil', 'status', 'ug', 'height', 'basta', 'lamang', 'sila', 'Filipino', 'citizen', 'nga', 'nag-edad', 'og', '18', 'hangtod', '27', 'anyos', ',', 'subay', 'kini', 'sa', 'mga', 'bag-ong', 'kausaban', 'sa', 'kwalipikasyon', 'sa', 'mga', 'kandidato.', 'Niadtong', 'Setyembre', ',', 'gianunsyo', 'sa', 'Miss', 'Universe', 'organization', 'ang', 'mga', 'kausaban', 'sa', 'ilang', 'balaod', ',', 'diin', 'tugutang', 'makaapil', 'sa', 'pageant', 'ang', 'mga', 'eligible', 'nga', 'inahan', 'ug', 'kadtong', 'mga', 'minyo', 'na', 'sugod', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 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, 3, 4, 4, 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, 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, 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]
|
cebuaner
|
4,456
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nipanaw', 'na', 'ang', 'singer', 'nga', 'si', 'Aaron', 'Carter', 'sa', 'edad', 'nga', '34', 'anyos.', 'Sumala', 'pa', 'sa', 'taho', 'sa', 'TMZ', ',', 'napalgang', 'patay', 'si', 'Carter', 'sa', 'bathtub', 'sulod', 'sa', 'iyang', 'panimalay', 'sa', 'Lancaster', ',', 'California', 'didto', 'sa', 'Estados', 'Unidos.', 'Naila', 'si', 'Carter', 'sa', 'iyang', 'mga', 'kanta', 'niadtong', 'late', ''90s', 'ug', 'early', '2000s.', 'Lakip', 'sa', 'iyang', 'sikat', 'nga', 'mga', 'kanta', 'mao', 'ang', '"', 'I', ''m', 'All', 'About', 'You.', '"', 'Si', 'Carter', 'mao', 'ang', 'manghud', 'ni', 'Nick', 'Carter', ',', 'kinsa', 'miyembro', 'sa', 'inilang', ''90s', 'boy', 'band', 'nga', 'Backstreet', 'Boys', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 5, 6, 0, 0, 1, 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, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0]
|
cebuaner
|
4,457
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakadawat', 'og', 'P100,000', 'cash', 'gift', 'ug', 'certificate', 'si', 'Ms.', 'Restituta', 'Partosa', ',', 'centenarian', 'awardee', 'sa', 'Zamboanguita', ',', 'gikan', 'sa', 'provincial', 'government', 'pinaagi', 'ni', 'Governor', 'Roel', 'Ragay', 'Degamo', 'ug', 'OIC-PSWDO', 'Rosa', 'Emilia', 'Banquerigo', 'karong', 'adlawa', ',', 'Nobyembre', '5', ',', '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.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 3, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,458
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DEPED', 'NAGPAHINUMDOM', 'SA', 'MGA', 'EMPLEYADO', 'NGA', 'LIKAYAN', 'ANG', 'RELASYON', ',', 'SOCIAL', 'MEDIA', 'CHATS', 'SA', 'MGA', 'ESTUDYANTE', 'GAWAS', 'SA', 'ESKWELAHAN', 'Nipagawas', 'og', 'kamandoan', 'ang', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'nga', 'nag-enumerate', 'sa', 'ilang', 'mga', 'palisiya', 'aron', 'mapalambo', 'ang', 'propesyonalismo', 'alang', 'sa', 'paghatod', 'sa', 'basic', 'education', 'programs.', 'Lakip', 'na', 'niini', 'ang', 'mga', 'instruksyon', 'sa', 'paglikay', 'sa', 'mga', 'relasyon', 'ug', 'interaksyon', 'sa', 'social', 'media', 'sa', 'mga', 'estudyante', 'gawas', 'sa', 'eskwelahan.', 'Gi-isyu', 'ang', 'Department', 'Order', '49', 'series', 'of', '2022', 'dated', 'November', '2', 'taliwala', 'sa', 'pagpatuman', 'og', 'full', 'in-person', 'classes', 'sa', 'mga', 'pampublikong', 'tunghaan.', 'Giamendar', 'sa', 'DO', '49', 'sa', 'DepEd', 'Order', '47', 'ug', 'nipaila', 'sa', 'bag-ong', 'seksyon', 'ug', 'gi-specify', 'ang', 'pipila', 'ka', 'mga', 'lagda', 'nga', 'subay', 'sa', 'probisyon', 'sa', 'Republic', 'Act', 'No.', '6713', 'o', 'Code', 'of', 'Conduct', 'and', 'Ethical', 'Standards', 'for', 'Public', 'Officials', 'and', 'Employees.', 'Gipahinumduman', 'sab', 'sa', 'DepEd', 'ang', 'mga', 'empleyado', 'niini', 'bahin', 'sa', 'legal', 'restrictions', 'sa', 'paggamit', 'sa', 'social', 'media', ',', 'bisan', 'kung', 'pang', 'personal', 'use', 'o', 'professional', 'function', '.'] 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, 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, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,459
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Posible', 'nga', 'aduna'y', 'dagdag-bawas', 'sa', 'presyo', 'sa', 'petrolyo', 'sa', 'mosunod', 'nga', 'semana', ',', 'sumala', 'pa', 'sa', 'Department', 'of', 'Energy', '(', 'DOE', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0]
|
cebuaner
|
4,460
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', 'ADUNAY', 'PAMASKONG', 'HANDOG', 'NGA', 'P500', 'MATAG', 'USA', 'SA', '34,200', 'KA', 'INDIGENT', 'FAMILIES', ',', 'SENIOR', 'CITIZENS', ',', 'PWDs', 'Giaprobahan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ang', 'pagpatuman', 'sa', 'Pamaskong', 'Handog', 'diin', 'makadawat', 'og', 'P500', 'matag', 'usa', 'ang', '34,200', 'ka', 'indigent', 'families', ',', 'senior', 'citizens', 'ug', 'PWDs', 'karong', 'Disyembre', 'ning', 'tuiga.', 'Nigahin', 'ang', 'City', 'Government', 'og', 'P17.1', 'milyon', 'nga', 'pondo', 'aron', 'mahatagan', 'ang', 'giila', 'nga', '34,200', 'ka', 'mga', 'benepisyaryo', 'gikan', 'sa', '30', 'ka', 'mga', 'barangay', 'sa', 'dakbayan.', 'Giawhag', 'ni', 'City', 'Social', 'Welfare', 'and', 'Development', 'Officer', 'Lilibeth', 'A.', 'Filipinas', 'ang', 'mga', 'representante', 'sa', 'indigent', 'families', ',', 'senior', 'citizens', '(', 'pensioner', 'or', 'non-pensioner', ')', 'ug', 'PWDs', 'nga', 'mosumite', 'sa', 'tanang', 'requirements', 'sa', 'wala', 'pa', 'ang', 'deadline', 'nga', 'gitakda', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'Barangay', 'Captains.', 'Kini', 'aron', 'tugutan', 'ang', 'mga', 'awtoridad', 'nga', 'ma-verify', 'ug', 'ma-finalize', 'ang', 'lista', 'sa', 'mga', 'makadawat', '.'] 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 3, 4, 4, 4, 4, 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]
|
cebuaner
|
4,461
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UTANG', 'SA', 'PILIPINAS', 'ANAA', 'NA', 'SA', 'P13.52', 'TRILYON', 'Anaa', 'na', 'sa', 'P13.52', 'trilyon', 'ang', 'utang', 'sa', 'nasudnong', 'gobyerno', 'sa', 'Pilipinas', 'sa', 'katapusang', 'bahin', 'sa', 'Setyembre.', 'Nisaka', 'kini', 'og', '4', '%', 'o', 'dugang', 'nga', 'P495.5', 'bilyon', 'tungod', 'sa', 'pag-us-os', 'sa', 'peso', 'ug', 'dugang', 'nga', 'pundo', 'aron', 'masuportahan', 'ang', 'budget', 'sa', 'nasud.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Bureau', 'of', 'the', 'Treasury', 'niadtong', 'Huwebes', ',', 'Nobyembre', '3', ',', '2022.', 'Sa', 'kinatibuk-ang', 'utang', ',', '31', '%', 'o', 'P4.22', 'trilyon', 'ang', 'utang', 'sa', 'foreign', 'lenders', ',', 'samtang', '69', '%', 'o', 'P9.3', 'trilyon', 'ang', 'giulos', 'gikan', 'sa', 'domestic', 'lenders.', 'Ang', 'mga', 'fluctuations', 'o', 'pag-usob-usob', 'partikular', 'na', 'ang', 'pagkunhod', 'sa', 'peso', 'batok', 'sa', 'US', 'dollar', ',', 'mao'y', 'nagpataas', 'sa', 'stock', 'sa', 'utang', 'ngadto', 'sa', 'P658.3', 'bilyon', 'o', '18.5', '%', '.'] 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, 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, 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]
|
cebuaner
|
4,462
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'Martes', ',', 'Nobyembre', '8', ',', '2022', ',', 'makasaksi', 'kita', 'sa', 'usa', 'ka', 'talagsaon', 'nga', 'Total', 'Lunar', 'Eclipse', 'karong', 'tuiga.', 'Makita', 'sab', 'ang', 'maong', 'eclipse', 'sa', 'nagkalain-laing', 'parte', 'sa', 'kalibutan', 'diin', 'ang', 'bulan', 'anaa', 'sa', 'ibabaw', 'sa', 'horizon', 'lakip', 'na', 'ang', 'Asia', ',', 'Australia', ',', 'North', 'America', ',', 'pipila', 'ka', 'bahin', 'sa', 'Northern', 'ug', 'Eastern', 'Europe', ',', 'ug', 'South', 'America.', 'Bes', ',', 'ayaw', 'palabya', 'ang', 'maong', 'oportunidad', 'tungod', 'dili', 'mahitabo', 'ang', 'sunod', 'nga', 'total', 'lunar', 'eclipse', 'hangtod', 'sa', 'Setyembre', '8', ',', '2025', '!'] 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, 5, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 5, 0, 5, 6, 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]
|
cebuaner
|
4,463
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KAPIN', 'P29-M', 'ANG', 'GIBANABANA', 'NGA', 'KADAOT', 'SA', 'BAGYONG', 'PAENG', 'SA', 'NEGROS', 'ORIENTAL', 'Gibutyag', 'sa', 'Provincial', 'Agriculturist', 'Office', 'nga', 'anaa', 'sa', 'P29,439,766.50', 'ang', 'gibanabanang', 'kadaot', 'nga', 'gibilin', 'sa', 'Severe', 'Tropical', 'Storm', 'o', 'Bagyong', 'Paeng', 'sa', 'sektor', 'sa', 'agrikultura', 'sa', 'Negros', 'Oriental.', 'Gipahibalo', 'ni', 'Provincial', 'Agriculturist', 'Emmanuel', 'Caduyac', 'ang', 'mga', 'miyembro', 'sa', 'Provincial', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Council', '(', 'PDRRMC', ')', 'atol', 'sa', 'virtual', 'council', 'meeting', 'niadtong', 'Martes', ',', 'Nobyembre', '1', ',', '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.
|
[0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 5, 6, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,464
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'hulagway', 'sa', 'mag-uyab', 'nga', 'nagsuot', 'sa', 'ilang', 'Halloween', 'costume', 'nga', 'inspired', 'sa', 'Netflix', 'Series', 'nga', 'Money', 'Heist.', 'Nag-pose', 'sila', 'sa', 'gawas', 'sa', 'buhatan', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', 'sa', 'Barangay', 'Daro', 'ug', 'uban', 'pang', 'lugar', 'sa', 'dakbayan', 'sa', 'Dumaguete', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,465
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'hulagway', 'sa', 'aktres', 'nga', 'si', 'Sofia', 'Andres', 'sa', 'dihang', 'nibisita', 'siya', 'sa', 'LGU', 'sa', 'Bayawan', 'City', 'ug', 'Pepe', ''s', 'Place.', '|', '📸', 'IbayawBayawan'] 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, 0, 0, 0, 0, 3, 0, 5, 6, 0, 5, 6, 6, 0, 0, 0]
|
cebuaner
|
4,466
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['121', 'NA', 'ANG', 'PATAY', ',', 'KAPIN', '3-M', 'KA', 'MGA', 'INDIBIDWAL', 'ANG', 'APEKTADO', 'TUNGOD', 'SA', 'BAGYONG', 'PAENG', 'Nisaka', 'ngadto', 'sa', '121', 'ang', 'natala', 'nga', 'mga', 'namatay', 'tungod', 'sa', 'Bagyong', 'Paeng', ',', 'mao', 'kini', 'ang', 'gibutyag', 'sa', 'National', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Council', '(', 'NDRRMC', ')', 'niadtong', 'Miyerkules', ',', 'Nobyembre', '2', ',', '2022.', 'Sumala', 'pa', 'sa', 'NDRRMC', 'sa', 'pinakabag-ong', 'situation', 'report', 'niini', ',', '92', 'sa', 'mga', 'namatay', 'ang', 'na-verify', 'na', 'samtang', 'ang', 'nabilin', 'nga', '29', 'padayon', 'pa', 'nga', 'gi-validate.', 'Anaa', 'sab', 'sa', '103', 'ang', 'numero', 'sa', 'mga', 'samdan', ',', 'samtang', '33', 'ka', 'mga', 'indibidwal', 'ang', 'nagpabilin', 'nga', 'missing.', 'Ang', 'mga', 'gikatahong', 'namatay', ',', 'anaa', 'sa', 'mga', 'rehiyon', 'sa', 'Cagayan', 'Valley', ',', 'Central', 'Luzon', ',', 'Bicol', ',', 'Calabarzon', ',', 'Mimaropa', ',', 'Western', 'Visayas', ',', 'Central', 'Visayas', ',', 'Eastern', 'Visayas', ',', 'Zamboanga', 'Peninsula', ',', 'Soccsksargen', ',', 'Bangsamoro', 'Autonomous', 'Region', 'for', 'Muslim', 'Mindanao', 'or', 'Barmm', ',', 'ug', 'Cordillera', 'Administrative', 'Region', '(', 'CAR', ')', '.', 'Sa', 'laing', 'bahin', ',', 'ang', 'kinatibuk-ang', 'numero', 'sa', 'mga', 'apektado', 'nga', 'indibidwal', 'anaa', 'na', 'sa', 'three-million', 'mark', 'nga', '3,180,132', 'o', '927,822', 'ka', 'mga', 'pamilya', 'sa', '7,341', 'ka', 'mga', 'barangay.', 'Matod', 'pa', 'sab', 'sa', 'NDRRMC', ',', 'aduna'y', 'mga', 'pamilya', 'nga', 'anaa', 'pa', 'sab', 'sa', 'mga', 'evacuation', 'centers.', 'Nagbilin', 'og', 'kadaot', 'ang', 'Bagyong', 'Paeng', 'sa', 'agrikultura', ',', 'imprastraktura', 'ug', 'mga', 'kabalayan.', 'Aduna', 'pa', 'sab', 'pipila', 'ka', 'mga', 'taytayan', 'ug', 'dalan', 'ang', 'dili', 'pa', 'maagian.', 'Dugang', 'pa', ',', '164', 'ka', 'mga', 'syudad', 'ug', 'munisipyo', 'ang', 'nideklara', 'og', 'state', 'of', 'calamity', '.'] 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 0, 5, 6, 6, 6, 6, 6, 0, 5, 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, 3, 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]
|
cebuaner
|
4,467
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DEPED', 'TUGUTAN', 'ANG', 'OPSYONAL', 'NGA', 'PAGSUL-OB', 'SA', 'FACE', 'MASK', 'SA', 'MGA', 'CLASSROOM', 'Sundon', 'sa', 'Department', 'of', 'Education', '(', 'DepEd', ')', 'ang', 'Executive', 'Order', 'No.', '7', 'sa', 'Malacañang', 'bahin', 'sa', 'boluntaryo', 'nga', 'pagsul-ob', 'sa', 'face', 'mask', 'sa', 'mga', 'indoor', 'spaces.', 'Magtugot', 'kini', 'sa', 'mga', 'estudyante', 'sa', 'pagtangtang', 'sa', 'ilang', 'mga', 'mask', 'samtang', 'nagtambong', 'sa', 'in-person', 'classes.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'DepEd', 'niadtong', 'Martes', ',', 'Nobyembre', '1', ',', '2022.', 'Sumala', 'pa', 'ni', 'DepEd', 'Spokesman', 'Michael', 'Poa', ',', 'mahimo', 'na', 'nga', 'ipatuman', 'dayon', 'sa', 'mga', 'tunghaan', 'ang', 'opsyonal', 'nga', 'pagsul-ob', 'og', 'face', 'mask', 'sa', 'sulod', 'nga', 'mga', 'luna.', 'Sa', 'niaging', 'semana', ',', 'giaprobahan', 'ni', 'President', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'boluntaryo', 'nga', 'pagsul-ob', 'sa', 'face', 'mask', 'sa', 'indoor', 'ug', 'outdoor', 'areas', 'taliwala', 'sa', 'nagpadayong', 'hulga', 'sa', 'Covid-19.', 'Ilalom', 'sa', 'maong', 'EO', ',', 'magpabilin', 'gihapon', 'nga', 'mandatory', 'ang', 'pagsul-ob', 'sa', 'face', 'mask', 'sa', 'mga', 'health', 'care', 'facilities', ',', 'medical', 'transport', 'vehicles', 'ug', 'tanang', 'klase', 'sa', 'public', 'transportation', '.'] 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, 3, 4, 4, 4, 4, 4, 0, 7, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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]
|
cebuaner
|
4,468
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna'y', 'kausbanan', 'sa', 'presyo', 'sa', 'gasolina', 'sa', 'PetroGazz', ',', 'Petron', ',', 'Pilipinas', 'Shell', 'ug', 'SeaOil', 'ugmang', 'adlawa', ',', 'Nobyembre', '1', ',', '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.
|
[0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,469
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['₱2.7-MILYON', 'NGA', 'GITUOHANG', 'SHABU', ',', 'NASAKMIT', 'SA', 'CADAWINONAN', 'Nasakmit', 'sa', 'mga', 'awtoridad', 'ang', 'gituohang', 'shabu', 'nga', 'mobalor', 'sa', '₱2,744,344.00', 'gikan', 'sa', 'gipahigayong', 'Buy-Bust', 'operation', 'sa', 'Purok', 'Star', 'Apple', ',', 'Barangay', 'Cadawinon', ',', 'Dumaguete', 'City', 'mga', '6:30', 'sa', 'gabii', 'niadtong', 'Oktubre', '28', ',', '2022.', 'Giila', 'sa', 'kapulisan', 'ang', 'suspek', 'nga', 'si', 'Marcelo', 'Rayoso', 'Amantillo', 'aka', '"', 'Junior', '"', ',', '42', 'anyos', 'ug', 'lumolupyo', 'sa', 'Barangay', 'Talay', 'sa', 'naasoy', 'nga', 'dakbayan', '.'] 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, 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, 0, 0, 1, 2, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0]
|
cebuaner
|
4,470
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daw', 'nahimong', '"', 'exodus', '"', 'ang', 'pagdasok', 'sa', 'mga', 'pasahero', 'sa', 'Maayo', 'Shipping', 'sa', 'pantalan', 'sa', 'Bato', ',', 'Samboan', ',', 'Cebu', 'niadtong', 'Sabado', ',', 'Oct.', '29', ',', '2022.', 'Kini', 'human', 'nga', 'nibalik', 'ang', 'mga', 'biyahe', 'sa', 'Negros', 'Oriental', 'sa', 'dihang', 'gilibkas', 'sa', 'Signal', 'No.', '1', 'sa', 'probinsya', 'tungod', 'sa', 'Bagyong', '#', 'PaengPH', '.'] 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, 3, 4, 0, 0, 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, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,471
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'nay', 'storm', 'signals', 'sa', 'Negros', 'Oriental', 'ug', 'Siquijor', 'samtang', 'nagpadayon', 'og', 'palayo', 'ang', 'Bagyong', '#', 'PaengPH', 'sa', 'maong', 'mga', 'probinsya.', 'Kini', 'sigon', 'sa', 'latest', 'nga', 'bulletin', 'gikan', 'sa', 'PAGASA', 'ganinang', 'alas-8', 'sa', 'gabii', ',', 'Oct.', '29', ',', '2022.', 'Apan', 'gipahimangnuan', 'gihapon', 'ang', 'publiko', 'nga', 'magmatngon', 'sa', 'mga', 'pag-ulan-ulan', 'sa', 'mga', 'mosunod', 'nga', 'oras', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 7, 8, 8, 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]
|
cebuaner
|
4,472
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'babaye', 'ang', 'kompirmadong', 'patay', 'sa', 'pagkuso-kuso', 'sa', 'Bagyong', '#', 'PaengPH', 'sa', 'Negros', 'Oriental.', 'Ang', 'biktima', ',', 'giila', 'nga', 'si', 'Elvie', 'Lambo', ',', 'kinsa', 'residente', 'sa', 'Sitio', 'Kansaluning', ',', 'Barangay', 'Pinalubngan', 'sa', 'lungsod', 'sa', 'Tayasan.', 'Sumala', 'pa', 'sa', 'NORPPO', ',', 'namatay', 'si', 'Lambo', 'human', 'siya', 'natumbahan', 'og', 'kahoy', 'taliwala', 'sa', 'pagbundak', 'sa', 'kusog', 'nga', 'ulan', 'kagahapong', 'adlawa', ',', 'Oct.', '28', ',', '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.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 5, 6, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,473
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LAING', 'BAGYO', 'GIKABALAK-AN', 'NGA', 'MOSULOD', 'SA', 'PAR', 'KARONG', 'LUNES', 'Wala', 'pa', 'nakatabok', 'sa', 'nasud', 'ang', 'Bagyong', '#', 'PaengPH', ',', 'apan', 'gikabalak-an', 'nga', 'mosulod', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'ang', 'laing', 'bagyo', 'o', 'Tropical', 'Depression', 'karong', 'Lunes', 'ug', 'posible', 'nga', 'mo-landfall', 'sa', 'Eastern', 'Visayas.', 'Tawagon', 'kini', 'nga', '"', 'QUEENIE', '"', 'sa', 'PAGASA', 'kung', 'makasulod', 'na', 'kini', 'sa', 'PAR.', 'Padayon', 'kini', 'nga', 'bantayan', 'sa', 'mga', 'mosunod', 'nga', 'adlaw', 'tungod', 'aduna', 'gihapon', 'posibilidad', 'nga', 'kini', 'magbag-o', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 7, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,474
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kung', 'plano', 'nimong', 'mobisita', 'sa', 'imong', 'mga', 'nitaliwan', 'nang', 'mga', 'minahal', 'sa', 'kinabuhi', 'karong', '#', 'Undas2022', ',', 'kinahanglan', 'kang', 'magdala', 'og', 'vaccination', 'card', 'aron', 'makasulod', 'sa', 'mga', 'sementeryo', 'ning', 'dakbayan', 'sa', 'Dumaguete.', 'Kini', 'subay', 'sa', 'kamanduan', 'nga', 'giluwatan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'bag-ohay', 'lamang.', 'Gawas', 'niini', ',', 'limitahan', 'sab', 'og', 'hangtod', '1', 'ka', 'oras', 'lamang', 'ang', 'pagbisita', 'sa', 'mga', 'sam-ang', 'ning', 'dakbayan.', 'Ang', 'maong', 'mga', 'polisa', ',', 'ipatuman', 'karong', 'kalag-kalag', 'aron', 'masiguro', 'nga', 'masunod', 'gihapon', 'ang', 'mga', 'health', 'protocol', 'taliwala', 'sa', 'pandemya', 'sa', '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.
|
[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, 5, 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, 7, 0]
|
cebuaner
|
4,475
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Halos', 'di', 'na', 'makita', 'sa', 'mapa', 'ang', 'Pilipinas', 'tungod', 'kay', 'kini', 'gitabunan', 'sa', 'mga', 'panganod', 'nga', 'dala', 'sa', 'Bagyong', '#', 'PaengPH.', 'Nagpabilin', 'sa', 'Signal', 'No.', '1', 'ang', 'Negros', 'Oriental', ',', 'Negros', 'Occidental', ',', 'ug', 'Siquijor', 'tungod', 'sa', 'maong', '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.
|
[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 5, 0, 0, 0, 0, 0]
|
cebuaner
|
4,476
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'naluwas', 'sa', 'baha', 'ang', 'Siaton', 'District', 'Hospital', 'karong', 'adlawa', ',', 'Oct.', '28', ',', '2022', ',', 'human', 'gikuso-kuso', 'sa', 'Bagyong', '#', 'PaengPH', 'ang', 'Negros', 'Oriental', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 0]
|
cebuaner
|
4,477
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Suspendido', 'na', 'ang', 'tanang', 'biyahe', 'sa', 'Maayo', 'Shipping', ',', 'Inc.', 'karong', 'adlawa', ',', 'Oct.', '28', ',', '2022', ',', 'human', 'nga', 'gipaubos', 'ang', 'Negros', 'Oriental', 'ug', 'Siquijor', 'sa', 'Signal', 'No.', '1', 'tungod', 'sa', 'hulga', 'sa', 'Bagyong', '#', 'PaengPH.', 'Kini', 'subay', 'sa', 'anunsyo', 'sa', 'maong', 'kompanya', 'karong', 'hapon', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,478
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BREAKING', ':', 'TIBUOK', 'NEGROS', 'ORIENTAL', ',', 'GIPAUBOS', 'NA', 'SA', 'SIGNAL', 'NO.', '1', 'TUNGOD', 'SA', 'BAGYONG', 'PAENG', 'Giisa', 'na', 'sa', 'PAGASA', 'ang', 'Tropical', 'Cyclone', 'Wind', 'Signal', 'No.', '1', 'sa', 'tibuok', 'Negros', 'Oriental', ',', 'Negros', 'Occidental', ',', 'ug', 'Siquijor', 'tungod', 'sa', 'hulga', 'sa', 'Tropical', 'Storm', '#', 'PaengPH.', 'Kini', 'sumala', 'pa', 'sa', 'latest', 'nga', 'weather', 'bulletin', 'ng', 'giluwatan', 'sa', 'ahensya', 'karong', 'alas-2', 'sa', 'hapon', 'Biyernes', ',', 'Oct.', '28', ',', '2022.', 'Tungod', 'niini', ',', 'giawhag', 'ang', 'mga', 'residente', 'sa', 'probinsya', 'nga', 'mag-andam', 'sa', 'mga', 'kusog', 'nga', 'hangin', 'ug', 'pag-ulan', 'nga', 'dala', 'sa', 'bagyo', 'sa', 'mga', 'mosunod', 'nga', 'oras.', 'Amping', 'karong', 'panahon', 'sa', 'bagyo', ',', 'beshie', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 5, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,479
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'na', 'maagian', 'ang', 'taytayan', 'sa', 'Fatima', ',', 'Sta.', 'Catalina', 'tungod', 'sa', 'lalom', 'ug', 'kusog', 'nga', 'baha', 'nga', 'dala', 'sa', 'Bagyong', '#', 'PaengPH', 'karong', 'Biyernes', ',', 'Oct.', '28', ',', '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.
|
[0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,480
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Taas', 'na', 'ang', 'lebel', 'sa', 'tubig', 'sa', 'Datag', ',', 'Siaton', 'taliwala', 'sa', 'pagkuso-kuso', 'sa', 'Bagyong', '#', 'PaengPH', 'sa', 'pipila', 'ka', 'bahin', 'sa', 'Negros', 'Oriental'] 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, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 5, 6]
|
cebuaner
|
4,481
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibaha', 'sab', 'ang', 'Negros', 'Oriental', 'State', 'University', '-', 'Siaton', 'Campus', 'karong', 'adlawa', ',', 'Oct.', '28', ',', '2022', ',', 'tungod', 'sa', 'mga', 'pag-ulan', 'nga', 'dala', 'sa', 'Bagyong', '#', 'PaengPH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,482
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kanselado', 'karon', 'ang', 'pipila', 'ka', 'biyahe', 'sa', 'OceanJet', 'gikan', 'ug', 'padulong', 'sa', 'Dumaguete', 'karong', 'adlawa', ',', 'Oct.', '28', ',', '2022', ',', 'tungod', 'sa', 'dili', 'maayong', 'panahon', 'nga', 'dala', 'sa', 'Bagyong', '#', 'PaengPH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,483
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DAGKONG', 'BALOD', ',', 'NASINATI', 'SA', 'KADAGATAN', 'SA', 'NEGROS', 'UG', 'SIQUIJOR', 'Daw', 'murag', 'roller', 'coaster', 'ang', 'biyahe', 'sa', 'usa', 'ka', 'barge', 'gikan', 'ug', 'Dumaguete', 'padulong', 'sa', 'Siquijor', 'karong', 'alas-8', 'sa', 'buntag', '(', 'Oct.', '28', ',', '2022', ')', ',', 'tungod', 'sa', 'mga', 'dagkong', 'balod', 'nga', 'dala', 'sa', 'Bagyong', '#', 'PaengPH', '.'] 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, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,484
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaubos', 'na', 'sa', 'Signal', 'No.', '1', 'ang', 'amihanang', 'bahin', 'sa', 'Negros', 'Oriental', 'tungod', 'sa', 'hulga', 'sa', 'Bagyong', '#', 'PaengPH.', 'Kini', 'sumala', 'pa', 'sa', 'latest', 'nga', 'weather', 'bulletin', 'sa', 'PAGASA', 'karong', 'alas-11', 'sa', 'buntag', ',', 'Oct.', '28', ',', '2022.', 'Mao', 'kini', 'ang', 'mga', 'dakbayan', 'sa', 'Guihulngan', 'ug', 'Canlaon', ',', 'ingon', 'man', 'mga', 'lungsod', 'sa', 'Vallehermoso', ',', 'La', 'Libertad', ',', 'Jimalalud', ',', 'ug', 'Tayasan.', 'Tungod', 'niini', ',', 'giabisuhan', 'ang', 'mga', 'residente', 'sa', 'mga', 'naasoy', 'nga', 'lugar', 'nga', 'mag-andam', 'sa', 'kusog', 'nga', 'hangin', 'ug', 'ulan', 'nga', 'masinati', 'didto', 'sulod', 'sa', 'mga', 'mosunod', 'nga', 'oras.', 'Una', 'nang', 'giisa', 'ang', 'Orange', 'Rainfall', 'Warning', 'sa', 'PAGASA', 'sa', 'Negros', 'Oriental', 'ug', 'Siquijor.', 'Buot', 'ipasabot', 'niini', ',', 'padayong', 'makasinati', 'ug', 'maulanong', 'panahon', 'ang', 'tibuok', 'probinsya', 'tungod', 'ni', 'Tropical', 'Storm', 'Paeng', '.'] 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, 5, 6, 0, 0, 0, 0, 7, 8, 8, 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, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,485
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AMPING', ',', 'GUIHULNGAN', '!', 'Magpabiling', 'suspendido', 'ang', 'mga', 'klase', 'sa', 'tanang', 'lebel', 'ug', 'tanang', 'mga', 'eskwelahan', '(', 'pribado', 'ug', 'pampubliko', ')', 'sa', 'tibuok', 'Guihulngan', 'City', 'karong', 'Huwebes', ',', 'Oct.', '27', ',', '2022', ',', 'tungod', 'sa', 'epekto', 'sa', 'trough', 'sa', 'Bagyong', '#', 'PaengPH', 'sa', 'maong', 'dakbayan.', 'Kini', 'sigon', 'sa', 'abiso', 'nga', 'giluwatan', 'ni', 'Mayor', 'Filomeno', 'Reyes', 'karong', 'gabii', ',', 'Oct.', '26.', 'Wala', 'sab', 'klase', 'sa', 'mga', 'lungsod', 'sa', 'Amlan', '(', 'all', 'levels', ')', 'ug', 'sa', 'Jimalalud', '(', 'elementary', 'to', 'high', 'school', ')', 'sa', 'Huwebes', ',', 'tungod', 'gihapon', 'sa', 'mga', 'pag-ulan', 'nga', 'dala', 'sa', 'maong', '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.
|
[0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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]
|
cebuaner
|
4,486
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lalom', 'nga', 'baha', 'niigo', 'sa', 'pipila', 'ka', 'bahin', 'sa', 'Guihulngan', 'City', 'karong', 'hapon', ',', 'Oct.', '26', ',', '2022', 'tungod', 'sa', 'way', 'puas', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'ikog', 'kon', 'trough', 'sa', 'Bagyong', '#', 'PaengPH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,487
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['347', 'TYPHOID', 'FEVER', 'CASES', 'NATALA', 'SA', 'NEGOR', ';', '4', 'ANG', 'NAMATAY', 'Nakatala', 'ang', 'probinsiya', 'sa', 'Negros', 'Oriental', 'og', '347', 'ka', 'mga', 'kaso', 'sa', 'typhoid', 'fever', 'gikan', 'sa', 'nagkalain-laing', 'disease', 'reporting', 'units', '(', 'DRUs', ')', 'gikan', 'Enero', '1', 'hantod', 'Oktubre', '22', ',', '2022.', 'Upat', 'sab', 'sa', 'maong', 'mga', 'kaso', 'ang', 'natala', 'nga', 'namatay.', 'Mas', 'taas', 'kini', 'og', '121', '%', 'kung', 'itandi', 'sa', 'samang', 'panahon', 'sa', 'niaging', 'tuig', 'nga', 'aduna'y', '157', 'cases', 'ug', '0', 'deaths.', 'Anaa', 'sa', '1', 'month', 'hangtod', '90', 'anyos', 'ang', 'edad', 'sa', 'mga', 'natala', 'nga', 'kaso.', 'Kalagmitan', 'nga', 'maapektaran', 'kadtong', 'mga', 'nag-edad', 'og', '40', 'anyos', 'pataas', '(', '34', '%', ')', '.', 'Kadaghanan', 'sab', 'nila', 'puros', 'mga', 'lalaki', '(', '51', '%', ')', '.'] 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, 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]
|
cebuaner
|
4,488
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU-BAGUIO', 'NGA', 'BIYAHE', 'MAGSUGOD', 'KARONG', 'DISYEMBRE', '16', 'Motanyag', 'ang', 'Philippine', 'Airlines', 'og', 'direct', 'flights', 'sa', 'Cebu', 'ug', 'Baguio', 'City', 'sugod', 'karong', 'Disyembre', '16', ',', '2022.', 'Ang', 'bag-ong', 'rota', ',', 'maghatag', 'og', 'hassle-free', 'travel', 'nga', 'duha', 'ka', 'oras', 'sa', 'City', 'of', 'Pines', 'gikan', 'sa', 'Queen', 'City', 'of', 'the', 'South', ',', 'ug', 'vice', 'versa.', 'Sugod', 'Disyembre', '16', ',', 'upat', 'ka', 'beses', 'kada', 'semana', 'mag-operate', 'ang', 'Cebu-Baguio-Cebu', 'flights', 'sa', 'PAL', 'uban', 'sa', 'sayon', 'nga', 'pagbiyahe', 'sa', 'buntag', ',', 'mao', 'ang', 'mosunod', ':', 'Giawhag', 'sab', 'sa', 'PAL', 'ang', 'tanan', 'nga', 'mga', 'pasahero', 'nga', 'i-check', 'ang', 'website', 'sa', 'ilang', 'arrival', 'point', 'alang', 'sa', 'mga', 'bag-ong', 'travel', 'requirements', 'ug', 'uban', 'pang', 'importante', 'nga', 'impormasyon', '.'] 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, 3, 4, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 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]
|
cebuaner
|
4,489
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pormal', 'nang', 'gideklarar', 'sa', 'Malacañang', 'nga', 'special', 'non-working', 'holiday', 'ang', 'Biyernes', ',', 'Oct.', '28', ',', '2022', ',', 'isip', 'pagsaulog', 'sa', 'Buglasan', '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.
|
[0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0]
|
cebuaner
|
4,490
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makita', 'ang', 'labing', 'pagdag-om', 'sa', 'kalangitan', 'dapit', 'sa', 'Freedom', 'Park', 'ning', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'hapon', ',', 'Oct.', '26', ',', '2022', ',', 'taliwala', 'sa', 'pagsaulog', 'sa', '#', 'Buglasan2022.', 'Giisa', 'karon', 'ang', 'orange', 'rainfall', 'warning', 'sa', 'pipila', 'ka', 'bahin', 'sa', 'Negros', 'Oriental', 'tungod', 'sa', 'mga', 'pag-ulan', 'nga', 'dala', 'sa', 'ikog', 'kon', 'trough', 'sa', 'Bagyong', '#', 'PaengPH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,491
|
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', 'Tayasan', 'karong', 'adlawa', ',', 'Oct.', '26', ',', '2022', ',', 'tungod', 'sa', 'way', 'puas', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'Bagyong', '#', 'PaengPH.', 'Mao', 'kini', 'ang', 'anunsyo', 'sa', 'mayor', 'sa', 'maong', 'lungsod', 'nga', 'si', 'Bimbo', 'Ruperto', 'karong', 'hapon.', 'Magpabilin', 'nga', 'walay', 'klase', 'sa', 'tanang', 'pampubliko', 'ug', 'pribadong', 'eskwelahan', 'sa', 'Tayasan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 5, 0, 0, 0, 0, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,492
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaubos', 'ang', 'habagatang', 'bahin', 'sa', 'Negros', 'Oriental', 'ug', 'Siquijor', 'sa', 'ORANGE', 'RAINFALL', 'WARNING', 'sa', 'PAGASA', 'tungod', 'sa', 'pag-ulan', 'nga', 'dala', 'sa', 'ikog', '(', 'trough', ')', 'sa', 'Bagyong', '#', 'PaengPH.', 'Gipasidaan', 'ang', 'publiko', 'nga', 'magmatngon', 'posibleng', 'pagbaha', 'ug', 'landslide', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,493
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KANHING', 'SOLICITOR', 'GENERAL', 'FLORIN', 'HILBAY', ',', 'GINGANLAN', 'NGA', 'BAG-ONG', 'DEAN', 'SA', 'SU', 'COLLEGE', 'OF', 'LAW', 'Ginganlan', 'isip', 'bag-ong', 'dean', 'sa', 'Silliman', 'University', '(', 'SU', ')', 'College', 'of', 'Law', 'si', 'kanhi', 'Solicitor', 'General', 'Florin', '"', 'Pilo', '"', 'T.', 'Hilbay', 'sugod', 'Nobyembre', '1', ',', '2022.', 'Mao', 'kini', 'ang', 'gikompirma', 'ni', 'SU', 'president', 'Dr.', 'Betty', 'C.', 'McCann', 'ug', 'niingon', 'nga', 'si', 'Hilbay', '"', 'will', 'bring', 'a', 'wealth', 'of', 'knowledge', 'and', 'experience', 'to', 'the', 'deanship', 'of', 'the', 'College', 'of', 'Law', ',', 'having', 'served', 'in', 'important', 'positions', 'in', 'government', ',', 'the', 'academe', ',', 'and', 'civil', 'society.', '"', 'Nipasalig', 'si', 'Hilbay', 'nga', 'gawas', 'sa', 'pagpalig-on', 'sa', 'posisyon', 'sa', 'SU', 'College', 'of', 'Law', 'isip', 'usa', 'sa', 'mga', 'top', 'law', 'schools', 'sa', 'Pilipinas', ',', 'tuyo', 'sab', 'niya', 'nga', 'manguna', 'sa', 'mga', 'cutting-edge', 'issues', 'sama', 'sa', ''Money', 'and', 'the', 'State', ',', ''', 'ug', 'gitumong', 'ang', 'pag-abot', 'sa', 'digital', 'currencies', 'ug', 'ang', 'epekto', 'niini', 'sa', 'balaod', 'ug', 'katilingban.', 'Nagsilbi', 'si', 'Hilbay', 'isip', 'solicitor', 'general', 'sa', 'termino', 'ni', 'kanhi', 'Presidente', 'Benigno', 'S.', 'Aquino', 'III', ',', 'ug', 'siya', 'sab', 'ang', 'nagsilbing', 'principal', 'agent', 'sa', 'Pilipinas', 'sa', 'United', 'Nations', 'Convention', 'on', 'the', 'Law', 'of', 'the', 'Sea', 'Arbitral', 'Proceedings', 'batok', 'sa', 'China', 'bahin', 'sa', 'isyu', 'sa', 'West', 'Philippine', 'Sea.', 'Nakuha', 'niya', 'ang', 'iyang', 'law', 'degree', 'sa', 'University', 'of', 'the', 'Philippines', '(', 'UP', ')', 'College', 'of', 'Law', 'niadtong', '1998', 'ug', 'nakuha', 'ang', 'iyang', 'Master', 'of', 'Laws', 'degree', 'sa', 'Yale', 'Law', 'School', 'niadtong', '2005.', 'Si', 'Hilbay', 'sab', 'ang', 'nahimong', 'No.', '1', 'sa', 'bar', 'examinations', 'niadtong', '1999.', 'Sa', 'wala', 'pa', 'siya', 'nahimong', 'solicitor', 'general', ',', 'nahimo', 'siyang', 'propesor', 'sa', 'UP', 'College', 'of', 'Law', 'diin', 'nagtudlo', 'siya', 'sa', 'Advanced', 'Constitutional', 'Litigation', ',', 'Constitutional', 'Law', ',', 'ug', 'Philosophy', 'of', 'Law.', 'Siya', 'sab', 'ang', 'nahimong', 'direktor', 'sa', 'Institute', 'of', 'Government', 'and', 'Law', 'Reform', 'sa', 'UP', 'Law', 'Center.', 'Gawas', 'sa', 'usa', 'siya', 'ka', 'Fulbright', 'Visiting', 'Scholar', 'sa', 'Boston', 'College', 'sa', 'United', 'States', ',', 'aduna', 'sab', 'siya'y', 'mga', 'panag-uban', 'sa', 'Max', 'Planck', 'Institute', 'for', 'Comparative', 'Public', 'Law', '&', 'International', 'Law', 'sa', 'Heidelberg', ',', 'Germany', ',', 'ug', 'sa', 'Asian', 'Law', 'Institute', 'for', 'Comparative', 'Public', 'Law', 'sa', 'National', 'University', 'sa', 'Singapore.', 'Nagsilbi', 'sab', 'siya', 'isip', 'editor-in-chief', 'sa', 'Philippine', 'Law', 'and', 'Society', 'Review', 'ug', 'editor', 'sa', 'Integrated', 'Bar', 'of', 'the', 'Philippines', 'Law', 'Journal.', 'Unang', 'gi-welcome', 'si', 'Hilbay', 'sa', 'SU', 'niadtong', '2006', 'diin', 'nag-lecture', 'siya', 'sa', 'College', 'of', 'Law.', 'Niadtong', 'panahona', ',', 'nagsilbi', 'siya', 'isip', 'Vice-Chairperson', 'sa', 'Bantay', 'Katarungan', '(', 'Sentinels', 'of', 'Justice', ')', ',', 'usa', 'ka', 'civic', 'organization', 'nga', 'giporma', 'ni', 'kanhi', 'Senator', 'Jovito', 'R.', 'Salonga', ',', 'diin', 'nitabang', 'siya', 'sa', 'Unibersidad', 'sa', 'pag-organisar', 'sa', 'Dr.', 'Jovito', 'R.', 'Salonga', 'Center', 'for', 'Law', 'and', 'Development.', 'Pulihan', 'ni', 'Hilbay', 'si', 'Atty.', 'Myles', 'Nicholas', 'G.', 'Bejar', ',', 'ang', 'University', 'General', 'Counsel', ',', 'kinsa', 'mao'y', 'gibutang', 'nga', 'moalagad', 'sa', 'nabilin', 'nga', 'panahon', 'sa', 'termino', 'ni', 'Dean', 'Sheila', 'Lynn', 'C.', 'Besario', 'human', 'siya', 'mabutang', 'sa', 'hudikatura.', '#', 'NewsBite', '|', 'with', 'reports', 'from', 'Silliman', 'University', 'website'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 2, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 5, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 3, 4, 4, 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, 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 7, 8, 8, 0, 3, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 6, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 3, 4, 0, 5, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 3, 4, 4, 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,494
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['"', 'ABI', 'GURO', 'ANI', 'NIYA', 'INSPECTOR', 'KO', '"', '🥲', 'Viral', 'karon', 'sa', 'social', 'media', 'ang', 'Silliman', 'University', 'student', 'nga', 'si', 'Demie', 'Haley', 'Reyes', 'human', 'siya', 'hapit', 'makalibre', 'og', 'plitihan', 'sa', 'Ceres', 'bus', 'tungod', 'kay', 'nasaypan', 'siya', 'nga', 'ticket', 'inspector.', 'Sumala', 'pa', 'ni', 'Reyes', ',', 'agi', 'kuno', 'kini', 'sa', 'yellow', 'nga', 'shirt', 'nila', 'sa', 'College', 'of', 'Business', 'Administration', 'nga', 'daw', 'kapareha', 'lang', 'sa', 'uniporme', 'sa', 'mga', 'inspector', 'sa', 'bus.', 'Pauli', 'na', 'unta', 'paingon', 'sa', 'Amlan', 'si', 'Reyes', 'sa', 'dihang', 'nabantayan', 'niya', 'nga', 'wala', 'siya', 'hatagi', 'og', 'ticket', 'bisan', 'pa', 'og', 'naabot', 'na', 'sila', 'dapit', 'sa', 'lungsod', 'sa', 'Sibulan.', 'Hinuon', ',', 'gitawag', 'niya', 'ang', 'konduktor', 'ug', 'gipangayuan', 'niya', 'kini', 'og', 'ticket', 'kay', 'matud', 'pa', 'niya', ',', '"', 'nanginabuhi', 'sab', 'baya', 'ni', 'sila'g', 'tarong.', '"', '"', 'Mao', 'ra', 'to', ',', 'amen', ',', '"', 'pagtapos', 'ni', 'Reyes', 'sa', 'iyang', 'post', 'nga', 'viral', 'karon', 'sa', 'Facebook.', 'Usa', 'ka', 'sophomore', 'student', 'karon', 'si', 'Reyes', 'sa', 'SU.', 'Nagkuha', 'siya', 'sa', 'kursong', 'BS', 'Accountancy', '.'] 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, 3, 4, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 5, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 7, 8, 0]
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cebuaner
|
4,495
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PRES.', 'MARCOS', ',', 'GILAOMANG', 'IMANDO', 'ANG', 'OPTIONAL', 'NGA', 'PAGSUL-OB', 'OG', 'FACE', 'MASKS', 'INDOORS', 'Gibutyag', 'ni', 'DOT', 'Sec.', 'Christina', 'Frasco', 'nga', 'ni-isyu', 'si', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'og', 'Executive', 'Order', 'nga', 'nagtinguha', 'sa', 'rekomendasyon', 'sa', 'IATF', 'bahin', 'sa', 'pagkuha', 'sa', 'mandatory', 'nga', 'pagsul-ob', 'sa', 'face', 'masks', 'sa', 'indoor', 'spaces.', 'Apan', 'kinahanglan', 'gihapon', 'nga', 'magsul-ob', 'og', 'face', 'masks', 'sa', 'public', 'transportation', ',', 'medical', 'transportation', 'ug', 'medical', 'facilities', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0, 0, 0, 0, 1, 2, 2, 0, 7, 8, 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]
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cebuaner
|
4,496
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGOR', 'GIIMBITAR', 'SI', 'PRES.', 'MARCOS', 'SA', 'PAGSAULOG', 'SA', 'BUGLASAN', 'FESTIVAL', 'Giimbitar', 'sa', 'provincial', 'government', 'si', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'aron', 'sa', 'pagsaksi', 'sa', 'Buglasan', 'Festival', 'ug', 'showdown', 'ug', 'street', 'dancing', 'karong', 'Oktubre', '30', 'sa', 'Dumaguete', 'City.', 'Sumala', 'pa', 'ni', 'Dr.', 'Nick', 'Elman', ',', 'co-chair', 'sa', 'Buglasan', 'Committee', ',', 'nagpadala', 'na', 'og', 'sulat', 'ang', 'Office', 'of', 'the', 'Governor', 'aron', 'pag-imbitar', 'sa', 'Presidente.', 'Dugang', 'pa', 'ni', 'Elman', ',', 'ang', 'pagbisita', 'sa', 'Presidente', 'mahimong', 'apil', 'sab', 'sila', 'si', 'First', 'Lady', 'Liza', 'Araneta-Marcos', ',', 'Cong.', 'Sandro', 'Marcos', 'sa', '1st', 'District', 'sa', 'Ilocos', 'Norte', ',', 'Sen.', 'Imee', 'Marcos-Manotoc', ',', 'ug', 'Sen.', 'Cynthia', 'Villar.', 'Usa', 'sa', 'labing', 'dako', 'ug', 'labing', 'gipaabot', 'nga', 'festival', 'ang', 'Buglasan', 'Festival', 'nga', 'gitakda', 'gikan', 'Oktubre', '21-30', 'uban', 'sa', 'kapin', '100', 'ka', 'mga', 'kalihukan', 'alang', 'sa', 'pagsaulog', 'niini.', 'Ang', 'maong', 'festival', ',', 'aduna'y', 'tema', 'karong', 'tuiga', 'nga', '"', 'Moving', 'Onward', 'NegOrenses', 'amidst', 'Challenges', '.', '"'] 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, 1, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 5, 6, 6, 6, 6, 0, 0, 1, 2, 0, 0, 0, 1, 2, 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]
|
cebuaner
|
4,497
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LAAG', 'NA', 'TA', 'SA', 'BUGLASAN', '!', 'Ubani', 'si', 'Aunty', 'Farrah', 'sa', 'iyang', 'pagsuroy', 'sa', 'mga', 'booth', 'sa', 'Pamplona', ',', 'Ayungon', ',', 'Tanjay', 'City', ',', 'ug', 'Sibulan', 'karong', 'Buglasan', '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.
|
[0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 6, 0, 0, 5, 0, 7, 0, 0]
|
cebuaner
|
4,498
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LAAG', 'NA', 'TA', 'SA', 'BUGLASAN', '!', 'Ubani', 'si', 'Aron', 'Tsupon', 'sa', 'iyang', 'pagsuroy', 'sa', 'mga', 'booth', 'sa', 'San', 'Jose', ',', 'Zamboanguita', ',', 'Mabinay', ',', 'Bindoy', ',', 'ug', 'Siaton', 'karong', 'Buglasan', '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.
|
[0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 0, 7, 0, 7, 0, 0, 7, 0, 7, 0, 0]
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cebuaner
|
4,499
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DGTE', 'GI-ADJUST', 'ANG', 'CURFEW', 'HOURS', ':', '2:00AM-4:00AM', 'MATAG', 'ADLAW', 'ATOL', 'SA', 'PAGSAULOG', 'SA', 'BUGLASAN', ',', 'CHARTER', ',', 'PASKO', 'Gi-adjust', 'ang', 'curfew', 'hours', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'gikan', '2:00am', 'hangtod', '4:00am', 'aron', 'hingpit', 'nga', 'makaapil', 'ang', 'mga', 'residente', 'ug', 'turista', 'sa', 'kapistahan', 'sa', 'Buglasan', ',', 'Charter', 'Anniversary', 'ug', 'pagsaulog', 'sa', 'Pasko.', 'Gipahiangay', 'kini', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ug', 'gikutlo', 'ang', 'dagko', 'nga', 'mga', 'panghitabo', 'sa', 'kataposang', 'kwarter', 'ning', 'tuiga.', 'Samtang', ',', 'makapadagan', '24', 'hours', '/', 'day', 'ang', 'mga', 'establisemento', 'nga', 'naghatag', 'og', 'mga', 'importanteng', 'serbisyo', 'sama', 'sa', 'apan', 'dili', 'limitado', 'sa', 'mga', 'convenience', 'store', ',', '24', '/', '7', 'nga', 'mga', 'restaurant', ',', 'botika', ',', 'ug', 'uban', 'pa', ',', 'basta', 'hingpit', 'ng', 'nabakunahan', 'ang', 'ilang', 'mga', 'kawani', 'ug', 'kustomer.', 'Ang', 'bisan', 'kinsang', 'tawo', 'nga', 'makalapas', 'sa', 'curfew', 'ug', 'dili', 'sakop', 'sa', 'mga', 'exceptions', ',', 'mahimong', 'dad-on', 'sa', 'Dumaguete', 'City', 'Police', 'Station', 'ug', 'makamulta', 'og', 'P3,000', 'o', '5', 'ka', 'adlaw', 'nga', 'pagkapriso.', 'Giawhag', 'sab', 'ang', 'Dumaguete', 'City', 'PNP', 'ug', 'Barangay', 'Peace', 'and', 'Safety', 'Officers', 'nga', 'ipatuman', 'ang', 'EO.', 'Mahimo', 'sab', 'nga', 'duolon', 'sa', 'BPSOs', 'kadtong', 'mga', 'indibidwal', 'nga', 'makalapas', 'sa', 'maong', 'EO', 'ug', 'Ordinansa', 'sulod', 'sa', 'ilang', ''area', 'of', 'jurisdiction', '.', '''] 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, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 4, 4, 0, 0, 0, 7, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0]
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cebuaner
|
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