Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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4,800 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Religious', 'man', 'gud', 'ko', ',', 'giampo', 'nako', 'nga', 'unta', 'akoa', 'na', 'lang', 'ka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,801 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Facebook', 'post', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'nakaani', 'og', 'lain-laing', 'komento', 'ug', 'reaksyon', 'sa', 'mga', 'netizen', 'karong', 'Biyernes', ',', 'Hunyo', '23', ',', 'diin', 'matud', 'pa', 'ni', 'Rendon', 'nga', 'kontra', 'siya', 'sa', 'mga', 'tawo', 'nga', 'wala', 'mag-organisa', 'sa', 'ilang', 'kaugalingong', 'kinabuhi.', 'komento', 'sa', 'usa', 'ka', 'netizen.', 'komento', 'pa', 'sa', 'usa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,802 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LOVI', 'POE', ',', 'GISULTIHAN', 'NI', 'TONI', 'FOWLER', 'Mao', 'kini', 'ang', 'gisulti', 'sa', 'social', 'media', 'personality', 'ug', 'aktres', 'nga', 'si', 'Toni', 'Fowler', 'bahin', 'sa', 'iyang', 'kalokohan', 'sa', 'aktres', 'nga', 'si', 'Lovi', 'Poe', 'sa', 'usa', 'ka', 'eksena', 'sa', 'teleseryeng', ''Batang', 'Quiapo.', ''', 'Nanamilit', 'kuno', 'siya', 'sa', 'assistant', 'director', 'nga', 'si', 'Coco', 'Martin', 'sa', 'wala', 'pa', 'kini', 'buhata.', 'Nangatarungan', 'si', 'Lovi.', 'Niabot', 'na', 'sa', '1.5', 'million', 'views', 'ang', 'prank', 'ni', 'Toni', 'kang', 'Lovi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0] | cebuaner |
4,803 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KUNG', 'AKO', ''YAN', ',', 'KAKASUHAN', 'KO', ''YAN', 'Padayon', 'nga', 'gibiaybiay', 'ang', 'mga', 'personalidad', 'sa', 'social', 'media', 'nga', 'sila', 'si', 'Xander', 'Ford', 'ug', 'Christian', 'Merck', 'Grey', ',', 'mga', 'iladong', 'personalidad', 'sa', 'social', 'media.', 'Bisan', 'kung', 'napildi', 'si', 'Xander', 'Ford', 'sa', '"', 'Battle', 'of', 'the', 'YouTubers', ',', '"', 'adunay', 'bag-ong', 'isyu', 'tali', 'nilang', 'duha.', 'Sa', 'Facebook', 'video', 'ni', 'Xander', ',', 'iyang', 'gipakita', 'ang', 'iyang', 'bag-ong', 'samad', 'sa', 'ulo.', 'Nakurat', 'si', 'Gena', 'Mago', ',', 'uyab', 'ni', 'Xander', ',', 'dihang', 'may', 'nanuktok', 'sa', 'pultahan', 'ug', 'miingon', 'nga', 'nagkadugo', 'si', 'Xander.', 'Si', 'Xander', 'Ford', 'gikulata', 'kuno', 'ni', 'Christian', 'Merck', ',', 'mapasaka', 'ba', 'kaha', 'siya', 'sa', 'kaso', '?', '|', 'Inquirer', 'Bandera'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 1, 2, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,804 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Isip', 'usa', 'ka', 'awardee', 'sa', ''Men', 'Who', 'Matter', ''', '2023', ',', 'gipasiugda', 'sa', 'PeopleAsia', 'ang', 'mga', 'paningkamot', 'ni', 'Alfred', 'Vargas', ',', 'kinsa', 'nagdala', 'og', 'tabang', 'sa', 'liboan', 'ka', 'mga', 'Pilipino', ',', 'partikular', 'ang', '93', 'ka', 'mga', 'balaod', 'ug', '1,200', 'ka', 'mga', 'proposal', 'nga', 'iyang', 'gi-author', 'o', 'gisuportahan', 'samtang', 'usa', 'ka', 'kongresista', ',', 'dugang', 'pa', 'sa', 'daghang', 'mga', 'sumasakay', ',', 'PWDs', ',', 'ug', 'mga', 'pasyente', 'sa', 'kanser', 'nga', 'kanunay', 'niyang', 'gisuportahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,805 | 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', 'reaksyon', 'sa', 'Filipino', 'import', 'nga', 'si', 'Kairi', 'Rayosdelsol', 'nga', 'moduwa', 'isip', 'import', 'sa', 'Indonesia', 'alang', 'sa', 'iladong', 'ONIC', 'Esports', 'team', 'sa', 'Mobile', 'Legends', ':', 'Bang', 'Bang', 'Professional', 'League', '(', 'MPL', ')', 'didto.', 'Si', 'Kairi', 'usa', 'sa', 'mga', 'Filipino', 'ML', ':', 'BB', 'players', 'nga', 'miadto', 'sa', 'gawas', 'sa', 'nasud', ',', 'ug', 'bisan', 'pa', 'sa', 'mga', 'hagit', ',', 'ang', '17-anyos', 'nga', 'jungler', 'nagtuo', 'nga', 'ang', 'iyang', 'pagbalhin', 'sa', 'Indonesia', 'takos.', 'matod', 'ni', 'Kairi', 'human', 'gitabangan', 'sa', 'ONIC', 'nga', 'makadaog', 'sa', 'Mobile', 'Legends', 'Southeast', 'Asia', 'Cup', '(', 'MSC', ')', '2023', 'championship', 'batok', 'sa', 'Blacklist', 'International', ',', 'iyang', 'mga', 'kaubang', 'Pinoy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 4, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 7, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 7, 0] | cebuaner |
4,806 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', '47-anyos', 'nga', 'Macarine', ',', 'usa', 'ka', 'Surigaonon', 'ug', 'kasamtangang', 'Provincial', 'Prosecutor', 'sa', 'Bohol', 'ang', 'mosuway', 'paglangoy', 'tabok', 'sa', 'Bugtong', 'Island', 'paingon', 'sa', 'Matayum', ',', 'Cataingan', 'sa', 'Masbate.', 'Kini', 'nga', 'pagsulay', 'maoy', 'usa', 'sa', 'mga', 'highlight', 'sa', 'Bagat', 'Dagat', 'Festival', '2023', 'sa', 'Masbate', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 7, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 5, 0] | cebuaner |
4,807 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'kaylap', 'nga', 'paggamit', 'sa', 'teknolohiya', 'sa', 'tanang', 'negosyo', 'sa', 'Pilipinas', ',', 'importante', 'ang', 'pagtutok', 'sa', 'pagpalambo', 'sa', 'digital', 'literacy', 'sa', 'mga', 'trabahante.', 'Pinaagi', 'sa', 'Philippine', 'Digital', 'Workforce', 'Competitive', 'Act', '(', 'RA', '11927', ')', 'nga', 'gi-author', 'ni', 'Sen.', 'Sonny', 'Angara', ',', 'ang', 'Digital', 'Workforce', 'Week', 'ipahigayon', 'matag', 'ikatulong', 'semana', 'sa', 'Hunyo', ',', 'diin', 'ipahigayon', 'ang', 'nagkalain-laing', 'mga', 'programa', 'ug', 'kalihukan', ',', 'pinangulohan', 'sa', 'Department', 'of', 'Information', 'and', 'Communications', 'Technology', '(', 'DICT', ')', '.', 'Naglakip', 'kini', 'sa', 'mga', 'may', 'kalabutan', 'sa', 'pagpalambo', 'ug', 'pagdesinyo', 'sa', 'web', ',', 'online', 'nga', 'pagtudlo', 'ug', 'pagtudlo', ',', 'animation', ',', 'paghimo', 'sa', 'sulud', ',', 'digital', 'marketing', ',', 'pagpalambo', 'sa', 'mobile', 'application', ',', 'pag-optimize', 'sa', 'search', 'engine', ',', 'ug', 'virtual', 'nga', 'tabang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,808 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'OFW', 'nga', 'taga', 'Leyte', 'ang', 'nag-trending', 'online', 'tungod', 'sa', 'prangka', 'ug', 'kataw-anan', 'nga', 'mensahe', 'nga', 'iyang', 'gisuwat', 'sa', 'iyang', 'balikbayan', 'box', 'nga', 'iyang', 'ipadala', 'sa', 'iyang', 'pamilya', 'sa', 'Pilipinas.', '“Para', 'sa', 'tanan', '(', 'silingan', 'ug', 'kaila.', ')', 'Tanang', 'naa', 'sa', 'sulod', 'kay', 'naka', 'pangalan', 'ug', 'para', 'kang', 'kinsa', ',', 'akoy', 'magbbuot', 'kay', 'ako', 'ang', 'nagpadala.', 'So', 'dili', 'mangluod', 'ang', 'pikas', 'ngabil', 'ha', ',', 'labi', 'nag', 'wala', 'kay', 'ambag', 'atong', 'pagpapangabroad', 'nako.', 'Unta', 'nasabtan', 'ra', ',', 'ug', 'wala', 'matagae', 'nako', 'ug', 'tsokolet', 'di', 'lang', 'malain', 'ha', ',', 'kay', 'remember', ',', 'BITTER', 'ka', 'sa', 'ako', 'tong', 'wala', 'pa', 'ka', 'abroad', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,809 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ginpahayag', 'ni', 'Iloilo', 'City', 'Mayor', 'Jerry', 'Treñas', 'ang', 'iya', '“disappointment”', 'kay', 'Vice', 'Mayor', 'El', 'Cid', 'Familiaran', 'sang', 'Bacolod', 'City', 'tuhoy', 'sa', 'isyu', 'sang', 'mga', 'Badjao', 'halin', 'sa', 'Bacolod', 'nga', 'nagtabok', 'pasulod', 'sa', 'Iloilo', 'nga', 'wala', 'sang', 'nagakaigo', 'nga', 'permiso', 'gikan', 'sa', 'Iloilo', 'LGU.', 'saad', 'ni', 'Mayor', 'Jerry', 'Treñas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 5, 6, 0, 0, 0, 0, 0, 3, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2, 0] | cebuaner |
4,810 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'up-to-date', 'nga', 'impormasyon', ',', 'ang', 'Pilipinas', 'Today', 'padayon', 'usab', 'nga', 'naghatod', 'og', 'cash', 'ug', 'gift', 'voucher', 'sa', 'atong', 'mga', 'followers', ',', 'ug', 'sa', 'mga', 'makahimamat', 'sa', 'atong', 'kauban', 'nga', 'si', 'Jing', 'Castañeda', 'sa', 'nagkalain-laing', 'lugar', 'sa', 'Metro', 'Manila.', 'Bantayi', 'ang', 'sunod', 'nga', 'Pilipinas', 'Today', 'Live', 'ug', 'basin', 'ikaw', 'na', 'ang', 'sunod', 'nga', 'mahatagan.', 'Tutok', 'lang', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 5, 6, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,811 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Grabe', 'ka', 'Mae', '!', '#', 'PilipinasToday'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0] | cebuaner |
4,812 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakighinabi', 'kang', 'Solis', ',', 'human', 'sa', 'desisyon', 'daw', 'nibunot', 'na', 'usab', 'sila', 'og', 'tunok', 'tungod', 'kay', 'dugay', 'na', 'kining', 'gihatagan', 'og', 'desisyon', 'nga', 'ibalhin', 'sa', 'prisohan.', 'Samtang', 'walay', 'impormasyon', 'karon', 'sa', 'sitwasyon', 'ni', 'alyas', 'altar', 'boy', 'kinsa', 'kauban', 'ni', 'alyas', 'Janice', 'sa', 'krimen.', 'Nasayran', 'nga', 'gilalisan', 'na', 'ang', 'Juvenile', 'Justice', 'Law', 'kon', 'angay', 'bang', 'ibalhog', 'sa', 'regular', 'nga', 'prisohan', 'ang', 'duha', 'ka', 'mga', 'sad-an', 'tungod', 'kay', 'mga', 'menor', 'de', 'edad', 'pa', 'ang', 'ilang', 'nahimo.', 'Salamat', 'Ginoo', ',', 'justice', 'has', 'been', 'served', '!', 'SOURCE', ':', 'https', ':', '/', '/', 'pilipinastoday.tv', '/', '3NuQSRY', '#', 'PilipinasToday', '#', 'MaguadSibling'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,813 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hunyo', '17', 'dihang', 'gibalikbalik', 'ni', 'Senador', 'Sonny', 'Angara', 'ang', 'pagpatuman', 'sa', 'iyang', 'co-authored', 'nga', 'Salary', 'Standardization', 'Law', 'V', 'nga', 'naghatag', 'og', 'upat', 'ka', 'hut-ong', 'sa', 'usbaw', 'sa', 'suweldo', 'ug', 'tinuig', 'nga', 'mid-year', 'bonus', 'sa', 'mga', 'kawani', 'sa', 'gobyerno.', 'Ang', 'ikaupat', 'ug', 'katapusang', 'tranche', 'sa', 'usbaw', 'sa', 'sweldo', 'gipatuman', 'karong', 'tuiga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,814 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tapos', 'nagsige', 'mog', 'hatag', 'og', 'lawog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,815 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gusto', 'ni', 'Senador', 'Sonny', 'Angara', 'nga', 'ang', 'kababayen-an', 'dili', 'limitado', 'sa', 'ilang', 'panginabuhian', 'ug', 'pinaagi', 'sa', 'Act', 'Allowing', 'the', 'Employment', 'of', 'Night', 'Workers', 'Including', 'Women', ',', 'o', 'Republic', 'Act', '10151', ',', 'naggarantiya', 'sa', 'katungod', 'sa', 'kababayen-an', 'nga', 'manarbaho', 'sa', 'gabii', ',', 'ug', 'makadawat', 'sila', 'og', 'mga', 'benepisyo', 'nga', 'may', 'kalabutan', 'niini.', 'Ang', 'RA', '10151', 'nag-amendar', 'sa', 'Articles', '130', 'ug', '131', 'sa', 'Labor', 'Code', 'of', 'the', 'Philippines', 'nga', 'nagtakda', 'og', 'limitasyon', 'sa', 'mga', 'babaye', 'sa', 'pagtrabaho', 'sa', 'gabii', 'hangtod', 'sa', 'kaadlawon', 'sugod', 'niadtong', '1974', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 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, 7, 8, 0, 0, 7, 8, 0, 7, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,816 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paambit', 'sa', 'Maa', 'Police', 'Station', 'nga', 'ang', '25-anyos', 'nga', 'lalaki', 'residente', 'sa', 'Catalunan', 'Pequeño.', 'Matod', 'sa', 'usa', 'ka', 'babayeng', 'residente', 'nga', 'nag-parking', 'daplin', 'sa', 'karsada', 'ang', 'sakyanan', 'nianang', 'pagkahapon', 'nga', 'nagdagan', 'ang', 'makina.', 'Paglabay', 'sa', 'usa', 'ka', 'oras', ',', 'nakabantay', 'nga', 'nakaparada', 'pa', 'ang', 'sakyanan', ',', 'apan', 'wala', 'na', 'moandar', 'ang', 'makina', ',', 'miduol', 'siya', 'sa', 'sakyanan', 'ug', 'mitan-aw', 'sa', 'sulod', ',', 'diin', 'iyang', 'nadiskubrehan', 'ang', 'drayber', 'nga', 'nagbuy-od', 'sa', 'iyang', 'gilingkoran', 'nga', 'wala', 'nay', 'kinabuhi.', 'Ang', 'mga', 'rescuer', 'nakahukom', 'nga', 'gubaon', 'ang', 'kilid', 'nga', 'bintana', 'sa', 'sakyanan', 'sa', 'pasahero', 'nga', 'bahin', 'kay', 'ang', 'mga', 'pultahan', 'sa', 'sulod', 'hingpit', 'nga', 'gi-lock', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,817 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Trending', 'karon', 'sa', 'netizens', 'ang', 'Facebook', 'post', 'sa', 'vlogger-actress', 'nga', 'si', 'Toni', 'Fowler', 'karong', 'Huwebes', ',', 'Hunyo', '22', ',', 'nga', 'nag-feature', 'sa', 'iyang', 'mga', 'litrato', 'nga', 'may', 'background', 'nga', ''Battle', 'of', 'the', 'YouTubers', ''', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0] | cebuaner |
4,818 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kung', 'unsa', 'man', 'ang', 'imong', 'naagian', 'karon', ',', 'sala', 'na', 'nimo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,819 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SHOT', 'PUNO', 'Mao', 'ba', 'kini', 'ang', 'reaksyon', 'sa', 'singer', 'nga', 'si', 'Juan', 'Karlos', 'Labajo', 'sa', 'birthday', 'greetings', 'ni', 'Miss', 'Universe', 'Philippines', 'First', 'Runner', 'Up', '2023', 'Maureen', 'Wroblewitz', 'ngadto', 'sa', 'iyang', 'bag-ong', 'rumored', 'boyfriend', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,820 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Dapitan', 'City', 'Tourism', 'Officer', 'Apple', 'Marie', 'Agolong', 'niingon', 'nga', 'usa', 'lang', 'kini', 'sa', 'mga', 'selebrasyon', 'nga', 'ipahigayon', 'sa', 'dakbayan', 'sa', 'ika-60', 'nga', 'anibersaryo', 'niini', 'isip', 'ka', 'siyudad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 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] | cebuaner |
4,821 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Gusto', 'ng', 'bansang', 'Amerika', 'na', 'sila', 'lang', 'ang', 'maghari', 'sa', 'sanlibutan', ',', '”', 'saad', 'ng', '‘Kingdom', 'of', 'Jesus', 'Christ’', 'founder', 'na', 'si', 'Pastor', 'Apollo', 'Quiboloy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 4, 4, 4, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,822 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'sa', 'mga', 'taho', ',', 'si', 'Elon', 'Musk', 'nag-tweet', 'karong', 'Miyerkules', ',', 'Hunyo', '21', ',', 'ang', 'iyang', 'hagit', 'human', 'nahibal-an', 'nga', 'si', 'Mark', 'Zuckerberg', 'nagplano', 'nga', 'maglunsad', 'og', 'laing', 'social', 'media', 'platform', 'nga', ''Thread', ',', ''', 'nga', 'giingon', 'nga', 'mas', 'maayo', 'nga', 'alternatibo', 'sa', 'Twitter.', 'tweet', 'ni', 'Musk', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 1, 0] | cebuaner |
4,823 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nag-viral', 'karong', 'adlawa', 'ang', 'King', 'of', 'Talk', ',', 'ang', 'pangutana', 'ni', 'Boy', 'Abunda', 'ngadto', 'sa', 'komedyante', 'nga', 'aktres', 'nga', 'si', 'Kakai', 'Bautista', 'sa', ''Fast', 'Talk', 'with', 'Boy', 'Abunda', ''', 'karong', 'Miyerkules', ',', 'Hunyo', '21', ',', 'diin', 'nangutana', 'si', 'Boy', 'kon', 'puwede', 'bang', 'ipaambit', 'ni', 'Kakai', 'ang', 'mga', 'butang', 'mahitungod', 'sa', 'Thai', 'actor', 'nga', 'si', 'Mario', 'Maurer.', 'Nitubag', 'si', 'Kakay', 'nga', 'kinsay', 'gipasabot', 'ni', 'Boy', ',', 'kuyog', 'ta', 'ug', 'lakaw.', 'dugang', 'ni', 'Kakai', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,824 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bro', ',', 'puwede', 'ra', 'man', 'dili', 'na', 'ka', 'moasa', 'pero', 'gahian', 'kaay'ka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,825 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FOOD', 'TRIP', 'SA', 'CLASSROOM', '!', 'Si', 'maestro', 'Jeric', 'Bocter', 'Maribao', 'wala', 'lang', 'mohatag', 'og', 'instruksiyon', ',', 'apan', 'naghatag', 'usab', 'siya', 'og', 'lamian', 'ug', 'makabusog', 'nga', 'pamahaw', 'sa', 'iyang', 'mga', 'tinun-an', 'matag', 'adlaw', ',', 'aron', 'mabusog', 'ang', 'ilang', 'tiyan', 'sa', 'dili', 'pa', 'magsugod', 'ang', 'ilang', 'klase.', '“Good', ''yan', ',', 'mas', 'lalong', 'gaganahan', 'pumasok', 'ang', 'mga', 'studyante.', 'Nakakagana', 'mag', 'aral', 'kapag', 'maayos', 'mag', 'dala', 'ng', 'studyante', 'ang', 'isang', 'guro', ',', '”', 'komento', 'sa', 'usa', 'ka', 'netizen.', 'dayeg', 'sa', 'lain', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 2, 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] | cebuaner |
4,826 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'babaye', 'nga', 'pasyente', 'anaa', 'karon', 'sa', 'isolation', 'sa', 'Anislas', 'Infirmary', 'Center', ',', 'sumala', 'ni', 'Albay', 'Governor', 'Grex', 'Lagman.', 'Si', 'Lagman', 'niingon', 'nga', 'ang', 'mga', 'opisyal', 'sa', 'panglawas', 'sa', 'probinsiya', 'nakaila', 'na', 'usab', 'sa', '30', 'ka', 'mga', 'tawo', 'nga', 'kompirmadong', 'adunay', 'suod', 'nga', 'kontak', 'sa', 'pasyente.', 'ingon', 'ni', 'Lagman', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 5, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,827 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi', 'luwa', 'nimo', 'kay', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,828 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['What', 'if', 'kanang', 'gikaon', 'nato', 'nga', 'manok', ',', 'naa', 'pud', 'na', 'silay', 'pangandoy', 'sa', 'kinabuhi', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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 |
4,829 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'ikaduhang', 'public', 'hearing', 'sa', 'Tatak', 'Pinoy', 'Bill', '(', 'Senate', 'Bill', 'No.', '2218', ')', ',', 'ang', 'tagsulat', 'sa', 'maong', 'balaudnon', ',', 'si', 'Sen.', 'Sonny', 'Angara', ',', 'nga', 'ang', 'Government', 'Procurement', 'Reform', 'Act', 'dili', 'igo', 'nga', 'makatabang', 'ug', 'adunay', 'epekto', 'sa', 'gisugyot', 'nga', 'suporta', 'sa', 'lokal', 'nga', 'industriya.', 'Gipasabot', 'niya', 'nga', 'dili', 'ang', 'locally-made', 'nga', 'mga', 'produkto', 'ang', 'gitutokan', 'tungod', 'kay', 'gitandi', 'kini', 'sa', 'kalidad', 'ug', 'presyo', 'sa', 'mga', 'imported', 'nga', 'produkto', 'nga', 'sikat', 'sa', 'mga', 'Pinoy.', '“Ang', 'gusto', 'namong', 'i-establisar', ',', 'isip', 'kabahin', 'sa', 'among', 'adbokasiya', 'sa', 'Tatak', 'Pinoy', ',', 'usa', 'ka', 'design', 'identity', 'para', 'sa', 'Pilipinas.', 'Gusto', 'namon', 'nga', 'maghimo', 'usa', 'ka', 'tatak', 'sa', 'Pilipinas', 'nga', 'labi', 'ka', 'inila', 'kaysa', 'karon.', 'Mahimo', 'naton', 'kini', 'pinaagi', 'sa', 'pagpadako', 'sa', 'mga', 'industriya', 'ug', 'paghimo', 'kanila', 'nga', 'labi', 'ka', 'kompetisyon', ',', 'labi', 'na', 'sa', 'merkado', 'sa', 'kalibutan', ',', '”', 'dugang', 'niya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 5, 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] | cebuaner |
4,830 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Police', 'Colonel', 'Noel', 'Aliño', ',', 'acting', 'director', 'sa', 'Bacolod', 'City', 'Police', 'Office', '(', 'BCPO', ')', ',', 'nipahibawo', 'karong', 'Miyerkules', ',', 'Hunyo', '21', ',', 'nga', 'direktang', 'makontak', 'siya', 'sa', 'mga', 'impormante.', 'matod', 'ni', 'Aliño.', 'Ang', 'pagdagmal', 'ug', 'pag-ihaw', 'sa', 'mga', 'iro', 'alang', 'sa', 'pagkaon', 'hugot', 'nga', 'gidili', 'ubos', 'sa', 'Animal', 'Welfare', 'Act', '(', 'Republic', 'Act', '8485', ')', 'ug', 'Anti-Rabies', 'Act', 'of', '2007', '(', 'RA', '9482', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 4, 4, 4, 4, 4, 4, 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, 7, 8, 8, 8, 8, 8, 8, 8, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0] | cebuaner |
4,831 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ako', 'na', 'nagtampo', ',', 'ako', 'pa', 'gihapon', 'manuyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,832 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Commission', 'on', 'Human', 'Rights', '(', 'CHR', ')', 'sa', 'Negros', 'Occidental', 'mipaambit', 'karong', 'Martes', ',', 'Hunyo', '20', ',', 'nga', 'ang', 'pasiunang', 'imbestigasyon', 'niini', 'sa', 'Himamaylan', 'City', 'massacre', 'nga', 'mipatay', 'sa', 'upat', 'ka', 'sakop', 'sa', 'pamilyang', 'Fausto', 'sa', 'Sitio', 'Kalingking', ',', 'Barangay', 'Buenavista', ',', 'niadtong', 'Hunyo', '14.', 'Giakusahan', 'sa', 'Philippine', 'Army', 'ug', 'New', 'People', ''s', 'Army', 'kung', 'kinsa', 'ang', 'responsable', 'sa', 'masaker', 'nga', 'mipatay', 'usab', 'sa', 'duha', 'ka', 'menor', 'de', 'edad', 'nga', 'mga', 'anak', 'sa', 'magtiayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 4, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 3, 4, 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] | cebuaner |
4,833 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Senador', 'Sonny', 'Angara', 'maoy', 'usa', 'sa', 'mga', 'tagsulat', 'sa', 'Mental', 'Health', 'Law', ',', 'nga', 'sukad', 'sa', 'pagkahimo', 'niini', 'niadtong', '2018', 'nagdala', 'na', 'og', 'barato', 'nga', 'serbisyo', 'sa', 'mental', 'health', 'sa', 'mga', 'Pilipino', ',', 'lakip', 'na', 'ang', 'konsultasyon', ',', 'therapy', ',', 'ug', 'tambal.', 'Hinuon', ',', 'gusto', 'ni', 'Senador', 'Sonny', 'nga', 'hingpit', 'nga', 'libre', 'ang', 'therapy', 'ug', 'mental', 'health', 'services', 'alang', 'sa', 'mga', 'Pilipino', ',', 'ilabi', 'na', 'sa', 'mga', 'kabus', ',', 'ug', 'iyang', 'giduso', 'nga', 'usbon', 'ang', 'Mental', 'Health', 'Law', 'pinaagi', 'sa', 'iyang', 'Senate', 'Bill', 'No.', '920', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 7, 8, 8, 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, 1, 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, 7, 8, 8, 0, 0, 0, 7, 8, 8, 8, 0] | cebuaner |
4,834 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'taho', ',', 'ang', 'biktima', 'miadto', 'sa', 'pulisya', 'sa', 'Sityo', 'Pamongot', 'sa', 'Barangay', 'Talagunton', 'aron', 'paisumbong', 'nga', 'sagpaon', 'unta', 'siya', 'sa', 'iyang', 'amahan.', 'Giingong', 'hubog', 'ang', 'amahan', 'dihang', 'nahitabo', 'ang', 'insidente.', 'Ang', 'bata', 'miingon', 'nga', 'gidala', 'siya', 'sa', 'usa', 'ka', 'lugar', 'og', 'ang', 'suga', 'gipalong.', 'Didto', 'na', 'dayon', 'gi', 'butangan', 'og', 'sili', 'iyang', 'pribado.', 'Nangayo', 'og', 'tabang', 'ang', 'lola', 'sa', 'biktima', 'aron', 'mailhan', 'ang', 'sad-an', ',', 'napakyas', 'ang', 'bata', 'sa', 'pagkuha', 'sa', 'ngalan', 'sa', 'suspek', 'apan', 'niingon', 'nga', 'mailhan', 'niya', 'kini', 'kung', 'makakita', 'siya', 'pag-usab.', 'Nipasaka', 'sila', 'og', 'reklamo', 'sa', 'women', 'and', 'children', ''s', 'protection', 'desk', 'sa', 'PNP', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
4,835 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'post', 'sa', 'usa', 'ka', 'netizen', 'nga', 'si', 'Crey', 'de', 'Luna', ',', 'niani', 'og', 'Laughing', 'trip', 'online', ',', 'human', 'niya', 'gipaambit', 'ang', 'istorya', 'sa', 'kamote', 'nga', 'gihatag', 'kaniya', 'sa', 'iyang', 'ex.', 'Nahisgotan', 'niya', 'sa', 'iyang', 'tweet', 'nga', 'gihatagan', 'kuno', 'siya', 'sa', 'iyang', 'ex', 'aron', 'mokalma', 'ang', 'iyang', 'tiyan', 'matag', 'atakehon', 'sa', 'iyang', 'acid', 'reflux.', 'Samtang', ',', 'gipaambit', 'ni', 'Crey', 'ang', 'iyang', 'nakat-onan', 'sa', 'Kamote', 'sa', 'iyang', 'ex.', 'Ikaw', ',', 'unsay', 'gihatag', 'sa', 'imong', 'ex', 'nga', 'nitubo', 'na', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,836 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giprayoridad', 'sa', 'Cebu-Cordova', 'Link', 'Expressway', 'Corporation', '(', 'CCLEC', ')', 'ni', 'Manny', 'V.', 'Pangilinan', 'ang', 'kaluwasan', 'sa', 'mga', 'motorista', ',', 'bikers', ',', 'ug', 'pedestrian', 'nga', 'moagi', 'sa', 'CCLEX', ',', 'busa', 'gibutangan', 'og', 'mga', 'emergency', 'call', 'box', 'aron', 'daling', 'makaresponde', 'ug', 'makatabang', 'sa', 'mga', 'lumalabay', 'sa', '8.9', 'ka', 'kilometro', 'nga', 'gilay-on', 'expressway.', 'Ang', 'mga', 'kahon', 'sa', 'tawag', 'sa', 'emerhensya', 'nahimutang', 'sa', 'mga', 'pag-abli', 'ug', 'mga', 'sidewalk', ',', 'ug', 'sa', 'mga', 'lugar', 'nga', 'paradahan', 'sa', 'emerhensya', 'sa', 'CCLEX', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 1, 2, 2, 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, 5, 0] | cebuaner |
4,837 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SO', 'UBOS', 'NA', 'ANG', 'PAGSABOT', 'NI', 'MAMI', 'ONI', '?', 'Sa', 'Instagram', 'story', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'karong', 'Huwebes', ',', 'Hunyo', '22', ',', 'iyang', 'gipaambit', 'ang', 'pamahayag', 'sa', 'vlogger-actress', 'nga', 'si', 'Toni', 'Fowler', ',', 'human', 'ang', 'naulahi', 'nitawag', 'kang', 'Rendon', 'nga', '"', 'the', 'most', 'plastic', 'influencer', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,838 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pamahayag', 'ni', 'Department', 'of', 'Education', '(', 'DepEd', ')', '-Region', '7', 'Director', 'Salustiano', 'Jimenez', 'niadtong', 'Martes', ',', 'Hunyo', '20', ',', 'siya', 'niingon', 'nga', 'kon', 'walay', 'probisyon', 'sa', 'sugyot', 'nga', 'mub-an', 'ang', 'gidaghanon', 'sa', 'mga', 'adlaw', 'sa', 'tingtungha', 'sa', 'matag', 'tuig', 'tingtungha', ',', 'ang', 'mga', 'tinun-an', 'ug', 'mga', 'magtutudlo', 'na', 'lang.', 'adunay', 'labing', 'menos', 'duha', 'ka', 'adlaw', 'nga', 'pahulay', 'sa', 'dili', 'pa', 'magsugod', 'sa', 'sunod', 'nga', 'tuig', 'sa', 'pagtungha', 'aron', 'masiguro', 'nga', 'ang', 'mga', 'klase', 'matapos', 'sa', 'Abril', 'o', 'Mayo.', 'Dugang', 'pa', 'niya', ',', 'adunay', 'balaod', 'nga', 'nag-ingon', 'nga', 'ang', 'gidaghanon', 'sa', 'mga', 'adlaw', 'sa', 'pagtungha', 'kinahanglang', 'dili', 'moubos', 'sa', '200', 'ug', 'dili', 'molapas', 'sa', '220', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,839 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INIT', 'KAAYO', 'BRETMAN', '!', 'Nagustohan', 'sa', 'mga', 'netizen', 'ang', 'post', 'sa', 'social', 'media', 'sa', 'Filipino-American', 'influencer', 'nga', 'nakabase', 'sa', 'Honolulu', ',', 'Hawaii', ',', 'ang', 'Bretman', 'Rock', ',', 'nga', 'nagpakita', 'sa', 'mga', 'litrato', 'niya', 'nga', 'nagsul-ob', 'og', 'all', 'gray', 'inspired', 'outfit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 5, 6, 6, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,840 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BOMB', 'SCARE', 'Mga', 'ginikanan', 'nagdali', 'sa', 'pagkuha', 'sa', 'ilang', 'mga', 'anak', 'sa', 'Sta.', 'Ana', 'Elementary', 'School', 'sa', 'Davao', 'City', 'human', 'sa', 'gikataho', 'nga', 'bomb', 'scare', 'niadtong', 'Huwebes', 'sa', 'buntag', ',', 'Hunyo', '22.', 'Ang', 'explosive', 'ordnance', 'disposal', '(', 'EOD', ')', 'sa', 'Davao', 'City', 'Police', 'ug', 'sa', 'Sta.', 'Gilayon', 'naman', 'nga', 'ginrespondehan', 'sang', 'Ana', 'Police', 'kag', 'ginronda', 'ang', 'eskwelahan.', 'Ang', 'mga', 'awtoridad', ',', 'bisan', 'pa', ',', 'wala’y', 'nakit-an', 'nga', 'timailhan', 'sa', 'usa', 'ka', 'bomba', 'ug', 'gideklarar', 'nga', 'negatibo', 'ang', 'hulga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 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] | cebuaner |
4,841 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pirme', 'na', 'lang', 'kuno', 'ko', 'nagbusangot.', 'Unsaon', '?', 'Kay', 'ang', 'usa', 'diha', 'happy', 'na', 'sa', 'uban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,842 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'hepe', 'sa', 'pulisya', 'nga', 'si', 'David', 'Smith', 'nagsulti', 'sa', 'lokal', 'nga', 'media', 'kaniadtong', 'Martes', 'nga', 'ang', '31-anyos', 'nga', 'si', 'Laura', 'Ilg', 'nanawag', 'sa', '911', 'kaniadtong', 'hapon', 'sa', 'Hunyo', '16.', 'ingon', 'ni', 'Smith.', 'Ang', 'mga', 'pulis', 'dali', 'nga', 'miabot', 'sa', 'balay', 'sa', 'Norwalk', ',', 'Ohio', ',', 'ug', 'si', 'Ilg', 'gidali', 'sa', 'pagdala', 'sa', 'ospital', ',', 'apan', 'ang', 'iyang', 'wala', 'pa', 'matawo', 'nga', 'bata', 'dili', 'maluwas', 'pagkahuman', 'sa', 'usa', 'ka', 'emerhensya', 'nga', 'c-section', ',', 'ingon', 'ni', 'Smith.', 'Namatay', 'usab', 'si', 'Ilg', 'pipila', 'ka', 'oras', 'ang', 'milabay', ',', 'dugang', 'niya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 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, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,843 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ibinunyag', 'ni', 'Pangulong', 'Ferdinand', '‘Bongbong’', 'Marcos', 'Jr.', 'ngayong', 'Huwebes', ',', 'Hunyo', '22', ',', 'na', 'agaran', 'nitong', 'pipirmahan', 'ang', 'panukalang', 'batas', 'na', 'Mahalika', 'Investment', 'Fund', 'Bill', 'kapag', 'dumating', 'na', 'ito', 'sa', 'kaniyang', 'opisina', 'sa', 'Malacañang.', 'Ayon', 'pa', 'sa', 'pangulo', ',', 'naka-depende', 'umano', 'sa', 'kung', 'sino', 'ang', 'mailalagay', 'sa', 'pwesto', 'kung', 'magiging', 'successful', 'ito.', 'saad', 'nito', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,844 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UYON', '?', 'Mao', 'kini', 'ang', 'komento', 'sa', 'TV', 'host-actress', 'nga', 'si', 'Maine', 'Mendoza', 'sa', 'interbyu', 'karong', 'Martes', ',', 'Hunyo', '20', ',', 'human', 'sa', 'ilang', 'Media', 'Day', 'sa', 'Legit', 'Dabarkads', ',', 'uban', 'sa', 'mga', 'kanhi', 'host', 'sa', ''Eat', 'Bulaga.', ''', 'Giingong', 'gidoble', 'ang', 'tanyag', 'kang', 'Maine', 'aron', 'lang', 'magpabilin', 'nga', 'Host', 'sa', ''Eat', 'Bulaga', ',', ''', 'pero', 'mas', 'gipili', 'pa', 'niini', 'ang', 'loyalty', 'nila', 'ni', 'Tito', ',', 'Vic', ',', 'ug', 'Joey', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0] | cebuaner |
4,845 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['sumala', 'ni', 'Ricky', 'Tijon', ',', 'Regional', 'Beneficiary', 'Data', 'Management', 'Officer', 'sa', 'Pantawid', 'Pamilya', 'Division', 'Davao', 'Field', 'Office.', 'Matod', 'ni', 'Tijon', 'nga', 'sa', 'Sept', '12-A', 'aduna', 'silay', 'target', 'nga', '73,598', 'nga', 'adunay', 'aktuwal', 'nga', '59,581', 'ka', 'tawo', 'ang', 'na-validate.', 'Diin', '46,565', 'na', 'karon', 'ang', 'adunay', 'active', 'client', 'status', 'diin', 'magsugod', 'na', 'sila', 'sa', 'pagsunod', 'sa', 'programa', 'karong', 'buwana.', 'Matod', 'ni', 'Margie', 'Cabido', ',', 'Division', 'Chief', ',', 'Pantawid', 'Pamilyang', 'Pilipino', 'Program', 'Davao', 'Field', 'Office', ',', 'gawas', 'sa', 'coverage', 'sa', 'kapin', 'usa', 'ka', 'gatos', 'ka', 'libo', 'ka', 'mga', 'panimalay', ',', 'ilang', 'gitan-aw', 'usab', 'ang', '30', '%', 'nga', 'pagtaas', 'sa', 'self-sufficiency', 'sa', 'mga', 'benepisyaryo', 'sa', 'katapusan', 'sa', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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, 1, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,846 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['saad', 'sa', 'usa', 'ka', 'fan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,847 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pinaagi', 'sa', 'Republic', 'Act', '9504', 'nga', 'co-author', 'ni', 'Sen.', 'Sonny', 'Angara', ',', 'gipatuman', 'ang', 'income', 'tax', 'exemption', 'alang', 'niadtong', 'nagsweldo', 'sa', 'minimum', 'nga', 'suholan', 'aron', 'mapagaan', 'ang', 'palas-anon', 'sa', 'buhis', 'sa', 'mga', 'ubos', 'og', 'kita.', 'Kay', 'naay', 'tax', 'exemption', ',', 'naay', 'extra', 'income', 'ug', 'mas', 'dako', 'ang', 'take-home', 'pay', 'sa', 'mga', 'empleyado', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 7, 8, 8, 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] | cebuaner |
4,848 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'tubag', 'sa', 'Miss', 'Grand', 'International', 'Philippines', '2023', 'contestant', 'nga', 'si', 'Herlene', 'Nicole', ''Hipon', 'Girl', ''', 'Budol', 'sa', 'interview', 'portion', 'sa', 'sashing', 'ceremony', 'nakaani', 'og', 'nagkadaiyang', 'reaksyon', 'gikan', 'sa', 'mga', 'netizen.', 'Matod', 'pa', 'sa', 'mga', 'netizen', 'nga', 'gi-exaggerate', 'ni', 'Budol', 'ang', 'kamatuoran', 'tungod', 'sa', 'iyang', 'tubag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 4, 4, 4, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,849 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'Philippine', 'Atmospheric', ',', 'Geophysical', 'and', 'Astronomical', 'Services', 'Administration', '(', 'PAGASA', ')', ',', 'alas', '5:28', 'sa', 'buntag.', 'pagsubang', 'sa', 'adlaw', 'ug', '6:28', 'p.m.', 'ang', 'gipaabot', 'nga', 'pagsalop', 'sa', 'adlaw', 'karong', 'Miyerkules', ',', 'Hunyo', '21', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,850 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Police', 'Major', 'Ivy', 'Bartolome', ',', 'hepe', 'sa', 'Argao', 'Police', ',', 'nga', 'nahitabo', 'ang', 'insidente', 'mga', 'alas', '9:30', 'sa', 'buntag', 'niadtong', 'Domingo', ',', 'Hunyo', '18', ',', 'sulod', 'sa', 'balay', 'sa', 'inahan', 'ug', '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. | [0, 0, 0, 0, 1, 2, 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] | cebuaner |
4,851 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'netizen', 'nga', 'si', 'Rissa', 'Ejedio', ',', 'inahan', 'sa', 'tres', 'anyos', 'ug', '10', 'ka', 'buwan', 'nga', 'bata', 'nakakita', 'sa', 'kapin', 'o', 'kulang', 'siyam', 'ka', 'bitin', 'nga', 'nagpuyo', 'sa', 'kisame', 'sa', 'lawak', 'sa', 'iyang', 'mga', 'anak.', 'Sa', 'Facebook', 'post', 'ni', 'Rissa', ',', 'iyang', 'gipaambit', 'nga', 'didto', 'siya', 'sa', 'kusina', 'dihang', 'nangayo', 'kaniya', 'og', 'tabang', 'ang', 'usa', 'sa', 'iyang', 'mga', 'anak', 'nga', 'makakuha', 'og', '"', 'dulaan', '"', 'sa', 'kisame.', 'Apan', 'nahibulong', 'si', 'Rissa', ',', 'dili', 'dulaan', 'ang', 'iyang', 'nakita', 'sa', 'kisame', 'kondili', 'pipila', 'ka', 'mga', 'halas.', 'Maayo', 'na', 'lang', 'kay', 'wala', 'naangol', 'ang', 'mga', 'bata', 'ug', 'daling', 'miabot', 'ang', 'tabang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,852 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Isip', 'usa', 'ka', 'advocate', 'sa', 'Tatak', 'Pinoy', 'ug', 'author', 'sa', 'Tatak', 'Pinoy', 'Bill', '(', 'Senate', 'Bill', 'No.', '2218', ')', ',', 'nagtuo', 'si', 'Sen.', 'Sonny', 'Angara', 'nga', 'importante', 'nga', 'tutokan', 'ug', 'palig-unon', 'ang', 'mga', 'industriya', 'sa', 'atong', 'nasud', 'aron', 'ang', 'mga', 'Pinoy', 'graduates', 'dinhi', 'na', 'magtrabaho', 'ug', 'dili', 'na', 'kinahanglang', 'mo-abroad', 'aron', 'makakita', 'og', 'disenteng', 'trabaho', 'ug', 'adunay', 'saktong', 'kita.', '“Kon', 'mag-pokus', 'lang', 'ta', 'sa', 'edukasyon', ',', 'magproduce', 'ra', 'ta', 'og', 'mga', 'graduate', 'nga', 'motrabaho', 'ra', 'sa', 'gawas', 'sa', 'nasod.', 'Tutokan', 'nato', 'ang', 'industriyalisasyon', 'aron', 'ang', 'atong', 'mga', 'graduates', 'adunay', 'trabaho', 'nga', 'maaplayan', 'sa', 'ilang', 'yutang', 'natawhan', ',', '”', 'matud', 'pa', 'ni', 'Senator', 'Sonny', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,853 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TINUOD', 'ANG', 'BALITA', '!', 'Sa', ''Fast', 'Talk', 'with', 'Boy', 'Abunda', ''', 'karong', 'Miyerkules', ',', 'Hunyo', '21', ',', 'gibutyag', 'sa', 'King', 'of', 'Talk', 'nga', 'si', 'Boy', 'Abunda', 'ang', 'pamahayag', 'sa', 'Chairman', 'ug', 'CEO', 'sa', 'GMA', 'network', ',', 'Inc.', ',', 'Felipe', 'L.', 'Gozon', ',', 'nga', 'malipayon', 'sila', 'nga', 'nahimong', 'kabahin', 'sa', ''It', ''s', 'Showtime', ''', '.', ''', 'Dugang', 'pa', 'ni', 'Gozon', 'nga', 'hapit', 'na', 'mahitabo', 'ang', 'ilang', 'contract', 'signing', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 1, 2, 2, 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] | cebuaner |
4,854 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nganong', 'mag-sorry', 'man', 'ko', 'niya', ',', 'eh', 'Pride', 'Month', 'man', 'karon', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0] | cebuaner |
4,855 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'pakigpulong', 'sa', 'graduation', 'ceremony', 'sa', 'Far', 'Eastern', 'University-Nicanor', 'Reyes', 'Medical', 'Foundation', ',', 'gipahinumdoman', 'ni', 'Alfred', 'Vargas', 'ang', 'mga', 'ni-graduate', 'nga', 'dili', 'kalimtan', 'ang', 'mga', 'nagalisod', 'ug', 'nanginahanglan', 'kung', 'moabot', 'sila', 'sa', 'punto', 'nga', 'makahigayon', 'sila', 'sa', 'pagtabang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 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] | cebuaner |
4,856 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mapasigarbuhon', 'nga', 'gipaambit', 'sa', 'usa', 'ka', 'summa', 'cum', 'laude', 'graduate', 'sa', 'West', 'Visayas', 'State', 'University', 'human', 'niya', 'gipaambit', 'ang', 'tribute', 'sa', 'iyang', 'lola', 'sa', 'iyang', 'valedictory', 'speech', 'Sa', 'report', 'ni', 'Mark', 'Salazar', 'sa', '"', '24', 'Oras', ',', '"', 'Lunes', ',', 'buta', 'ang', 'usa', 'ka', 'mata', 'sa', 'lola', 'ni', 'Yancy', 'Panugon', 'nga', 'si', 'Mamang', 'Norma', 'apan', 'nagkugi', 'kini', 'sa', 'pagpadako', 'niini.', 'Sa', 'dihang', 'gipangutana', 'kon', 'unsa', 'nga', 'importanteng', 'leksyon', 'ang', 'iyang', 'nakat-unan', 'gikan', 'sa', 'iyang', 'lola', ',', 'si', 'Yancy', 'miingon', 'nga', 'adunay', 'usa', 'ka', 'tambag', 'nga', 'dili', 'niya', 'makalimtan.', 'matod', 'ni', 'Yancy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
4,857 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'maoy', 'gipaambit', 'ni', 'Davao', 'City', 'Police', 'Major', 'Joenel', 'Pederio', ',', 'hepe', 'sa', 'Buhangin', 'Police', 'Station', ',', 'human', 'nila', 'gipangutana', 'ang', 'suspek.', 'Matod', 'ni', 'Pederio', ',', 'ilang', 'nabantayan', 'nga', 'nagkurog', 'ang', 'suspek', 'dihang', 'nitubag', 'sa', 'ilang', 'mga', 'pangutana', ',', 'dugang', 'sa', 'ilang', 'pangagpas', 'nga', 'ang', '70-anyos', 'gyud', 'ang', 'nag-unang', 'suspek', 'sa', 'pagpatay', 'kang', 'Shiela', 'Han-ay', 'Lagsaway', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 6, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 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, 1, 2, 2, 0] | cebuaner |
4,858 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Aw', 'nagrepair', 'diay', ',', 'abi', 'ko', 'ga', 'swimming', '"', 'Kini', 'ang', 'komedya', 'nga', 'komento', 'sa', 'usa', 'ka', 'netizen', 'sa', 'mga', 'litrato', ',', 'sa', 'laing', 'bahin', ',', 'ang', 'tubag', 'sa', 'netizen', 'nga', 'alang', 'kini', 'sa', 'kaluwasan', '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, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
4,859 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'pa', 'ni', 'Office', 'of', 'Civil', 'Defense', '(', 'OCD', ')', 'Administrator', 'Undersecretary', 'Ariel', 'Nepomuceno', ',', 'ang', 'mga', 'hingtungdan', 'nga', 'ahensya', 'sa', 'gobyerno', 'ug', 'local', 'government', 'units', '(', 'LGUs', ')', 'nakig-alayon', 'aron', 'matubag', 'ang', 'krisis', 'gumikan', 'sa', 'pagbuto', 'sa', 'Bulkang', 'Mayon', 'ug', 'masiguro', 'ang', 'kaayohan', 'sa', 'mga', 'residente', 'nga', 'apektado', 'niini.', 'ingon', 'ni', 'Nepomuceno', 'sa', 'public', 'briefing.', 'Ang', '90', 'ka', 'adlaw', 'nga', 'pagpangandam', ',', 'matod', 'ni', 'Nepomuceno', ',', 'base', 'sa', 'nangaging', 'mga', 'kasinatian', 'sa', 'kagamhanang', 'probinsiyal', 'sa', 'Albay', 'sa', 'dihang', 'mibuto', 'ang', 'Mayon', 'niadtong', '2014', 'ug', '2018', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0] | cebuaner |
4,860 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'opisyal', 'na', 'Twitter', 'account', 'ng', 'GTV', 'ng', 'GMA', 'Network', 'ay', 'nag-post', 'ng', 'art', 'card', 'ng', 'noontime', 'show', 'ng', 'ABS-CBN', 'na', '‘It’s', 'Showtime’', 'noong', 'Martes', ',', 'na', 'may', 'caption', 'na', ':'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,861 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'sa', 'gipa-isip', 'taka', ',', 'pero', 'basin', 'mao', 'dugay', 'siya', 'moreply', 'kay', 'nakigsulti', 'na', 'sa', 'uban', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,862 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakigpulong', 'ni', 'Quezon', 'City', '5th', 'District', 'Councilor', 'Alfred', 'Vargas', 'atol', 'sa', 'Independence', 'Day', 'Job', 'Fairs', 'niadtong', 'Hunyo', '12', 'sa', 'SM', 'Fairview', 'ug', 'SM', 'Novaliches', ',', 'iyang', 'gitambagan', 'ang', 'mga', 'nangita', 'og', 'trabaho', 'sa', 'duha', 'ka', 'nag-unang', 'hiyas', 'nga', 'gipangita', 'sa', 'mga', 'kompaniya', 'sa', 'ilang', 'mga', 'empleyado', ',', 'sa', 'pribadong', 'sektor', 'man', 'o', 'sa', 'gobyerno', 'gihapon.', '“Bisan', 'asa', 'ka', 'moadto', ',', '‘kung', 'naa', 'ka', ',', 'idugang', 'lang', 'imong', 'kahanas', ',', 'talento', 'ug', 'talento', ',', 'modako', 'ka', ',', '”', 'dugang', 'ni', 'Vargas'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5] | cebuaner |
4,863 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'utanon', 'ug', 'produkto', 'nga', 'gipang-apod-apod', 'gikan', 'sa', 'mga', 'grupo', 'sa', 'kababayen-an', 'nga', 'mag-uuma', 'nga', 'gipasiugdahan', 'ni', 'Albay', '3rd', 'District', 'Rep.', 'Fernando', 'Cabredo', 'pipila', 'ka', 'tuig', 'na', 'ang', 'milabay.', 'Ang', 'pondo', 'gikan', 'sa', 'opisina', 'ni', 'House', 'Speaker', 'Martin', 'Romualdez.', 'Una', 'nang', 'gikataho', 'nga', 'ang', 'buhatan', 'ni', 'Romualdez', 'nigahin', 'og', 'P1', 'milyones', 'nga', 'cash', 'ug', 'relief', 'packs', 'sa', 'matag', 'distrito', 'sa', 'Albay', ',', 'uban', 'sa', 'paningkamot', 'nga', 'mahatagan', 'ang', 'matag', 'distrito', 'og', 'P10', 'milyones', 'nga', 'cash', 'aid', 'gikan', 'sa', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', '.', 'Dul-an', 'sa', '39,000', 'ka', 'residente', ',', 'o', '10,000', 'ka', 'pamilya', ',', 'sa', 'Albay', 'ang', 'apektado', 'sa', 'pagbuto', 'sa', 'bulkan', ',', 'sumala', 'sa', 'pinakaulahing', 'datus', 'gikan', 'sa', 'National', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Council', '(', 'NDRRMC', ')', '.', 'Gi-report', 'usab', 'sa', 'ahensya', 'nga', '20,127', 'ka', 'mga', 'tawo', 'ang', 'gipabakwit', 'gikan', 'sa', 'danger', 'zone', 'sa', 'bulkan', ',', 'samtang', '18,892', 'nga', 'mga', 'residente', 'ang', 'nagpabilin', 'sa', 'mga', 'evacuation', 'center', ',', 'ug', 'ang', 'nahabilin', 'naa', 'sa', 'temporaryo', 'nga', 'mga', 'puy-anan', 'o', 'nagpabilin', 'sa', 'mga', 'paryente.', 'SOURCE', ':', 'https', ':', '/', '/', 'pilipinastoday.tv', '/', '4481exs', '#', 'PilipinasToday', '#', 'Albay', '#', 'MayonVolcano'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 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, 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] | cebuaner |
4,864 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Kapitan', 'Melvin', 'Mercado', ',', 'commander', 'sa', 'Police', 'Station', '8', ',', 'nagkanayon', 'nga', 'niadtong', 'Biyernes', ',', 'Hunyo', '16', ',', 'nasikop', 'ang', 'suspek', 'subay', 'sa', 'warrant', 'of', 'arrest', 'sa', 'kasong', 'statutory', 'rape', 'nga', 'giisyu', 'ni', 'Judge', 'Fernand', 'Castro', 'sa', 'Regional', 'Trial', 'Court', 'Branch', '41', ',', 'Bacolod', 'City', 'nga', 'walay', 'piyansa.', 'Matod', 'niya', ',', 'gipasakaan', 'na', 'og', 'kasong', 'pagpanglugos', 'ang', 'suspek', 'human', 'giingong', 'nanglugos', 'sa', 'iyang', 'pag-umangkon', 'sulod', 'sa', 'ilang', 'balay', 'karong', 'tuiga.', 'Dugang', 'pa', 'niya', 'nga', 'ang', 'biktima', ',', '12-anyos', ',', 'nangayo', 'og', 'tabang', 'sa', 'iyang', 'mga', 'ginikanan', 'ug', 'pasakaan', 'og', 'kaso', 'ang', 'suspek', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,865 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpa-blood', 'test', 'ko.', 'Ingon', 'sa', 'test', ',', 'ikaw', 'lang', 'type', 'ko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,866 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BBM', ',', 'DILI', 'GANAHAN', 'OG', 'FAKE', 'NEWS', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,867 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SALAMAT', ',', 'DOC', '!', ''', 'Nalipay', 'ang', 'mga', 'netizen', 'sa', 'tubag', 'sa', 'vlogger', 'nga', 'si', 'Toni', 'Fowler', 'sa', 'dihang', 'iyang', 'giklaro', 'ang', 'pamahayag', 'ni', 'kanhi', 'Manila', 'city', 'Mayor', 'ug', 'karon', 'vlogger', 'Isko', 'Moreno', 'sa', 'iyang', 'YouTube', 'vlog', 'niadtong', 'Hunyo', '16.', 'Giangkon', 'ni', 'Toni', 'nga', 'peke', 'ang', 'iyang', 'dughan', ',', 'ug', 'bisan', 'ang', 'ubang', 'parte', 'sa', 'iyang', 'lawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 1, 2, 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] | cebuaner |
4,868 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'paghatag', 'sa', 'unang', 'geographical', 'indication', '(', 'GI', ')', 'sa', 'mga', 'mangga', 'sa', 'Guimaras', ',', 'si', 'Sen.', 'Sonny', 'Angara', ',', 'isip', 'tagsulat', 'sa', 'Protected', 'Geographical', 'Indications', 'Bill', '(', 'Senate', 'Bill', '1868', ')', 'ug', 'manlalaban', 'sa', 'Tatak', 'Pinoy', ',', 'nga', 'maghimo', 'sa', 'mga', 'produkto', 'sa', 'Pinoy', 'nga', 'mas', 'mailhan', 'sa', 'internasyonal', 'nga', 'merkado.', 'Giaprobahan', 'sa', 'Intellectual', 'Property', 'Office', 'of', 'the', 'Philippines', '(', 'IPOPHL', ')', 'ang', 'aplikasyon', 'sa', 'Guimaras', 'Mango', 'Growers', 'and', 'Producers', 'Development', 'Cooperative', '(', 'GMGPDC', ')', 'aron', 'mahatagan', 'ang', 'produkto', 'og', 'GI', 'grant.', 'dugang', 'pa', 'ni', 'Senator', 'Sonny.', 'Niadtong', '2022', ',', 'nakadawat', 'usab', 'ang', 'GMGPDC', 'og', 'mga', 'tanyag', 'sa', 'eksport', 'gikan', 'sa', 'Czech', 'Republic', ',', 'UAE', ',', 'ug', 'South', 'Korea', 'human', 'ang', 'pag-eksport', 'sa', 'duha', 'ka', 'toneladang', 'mangga', 'sa', 'Guimaras', 'miigo', 'sa', 'Switzerland', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 1, 2, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0] | cebuaner |
4,869 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Roly', 'napalgang', 'patay', 'duol', 'sa', 'ilang', 'payag', 'niadtong', 'Hunyo', '14', ',', 'samtang', 'ang', 'lawas', 'sa', 'iyang', 'asawa', 'ug', 'mga', 'anak', 'nadiskubrehan', 'sa', 'sulod.', 'Sila', 'si', 'Roly', 'ug', 'Emelda', 'sakop', 'sa', 'Baclayan', ',', 'Bito', ',', 'Cabagal', 'Farmers', 'and', 'Farmworkers', 'Association', '(', 'BABICAFA', ')', '.', 'matod', 'sa', 'ahensya', 'karong', 'Sabado', 'sa', 'usa', 'ka', 'pamahayag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,870 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kung', 'biyaan', 'nimo', 'tali', 'sa', 'payong', 'o', 'panyo', ',', 'nganong', 'ako', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,871 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HAPPY', 'BIRTHDAY', ',', 'PEPE', '!', 'Gihandum', 'sa', 'mga', 'Pilipino', 'karong', 'Lunes', ',', 'Hunyo', '19', ',', '2023', ',', 'ang', 'ika-162', 'nga', 'anibersaryo', 'sa', 'pagkatawo', 'sa', 'atong', 'Pambansang', 'Bayani', ',', 'Dr.', 'Jose', 'P.', 'Rizal', ',', 'nga', 'natawo', 'niadtong', 'Hunyo', '19', ',', '1861', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,872 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['June', '18', ',', '2023.', 'HAPPY', 'FATHER’S', 'DAY', '!', 'Saludo', 'sa', 'mga', 'paningkamot', ',', 'sakripisyo', ',', 'ug', 'gugma', 'sa', 'atong', 'mga', 'haligi', 'sa', 'panimalay.', 'Mabuhi', 'ug', 'dugay', '!', 'Paambit', 'gikan', 'kay', 'Sen.', 'Sonny', 'Angara'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2] | cebuaner |
4,873 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['June', '18', ',', '2023.', 'ADLAW', 'SA', 'MGA', 'AMAHAN', '!', 'Sa', 'tunga-tunga', 'sa', 'selebrasyon', ',', 'ayaw', 'kalimot', 'sa', 'pagpasalamat', 'sa', 'walay', 'puas', 'nga', 'suporta', 'ug', 'pagsabot', 'sa', 'numero', 'unong', 'idolo', 'sa', 'pamilya.', 'Malipayong', 'Adlaw', 'sa', 'Amahan', '!', 'Paambit', 'gikan', 'kay', 'Alfred', 'Vargas'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 1, 2] | cebuaner |
4,874 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['June', '18', ',', '2023.', 'PALANGGA', 'TAKA', ',', 'TAY', '!', 'Ang', 'Pilipinas', 'Today', 'naghatag', 'pasidungog', 'sa', 'mga', 'superhero', 'sa', 'pamilya.', 'Tatay', ',', 'Tatang', ',', 'Papa', ',', 'Daddy', ',', 'Dada', ',', 'happy', 'Father’s', 'Day', 'po', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0] | cebuaner |
4,875 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Fake', 'pala', 'giatay', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,876 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGNA', 'MOM', 'LAUDE', 'Batan-ong', 'inahan', 'si', 'Jackilyn', 'Andres', 'apan', 'wala', 'siya', 'mohunong', 'sa', 'pagkab-ot', 'sa', 'iyang', 'pangandoy', 'nga', 'makagradwar', 'bisag', 'duna', 'na', 'siyay', 'kaugalingong', 'pamilya.', 'Sa', 'tabang', 'sa', 'mga', 'tawo', 'nga', 'iyang', 'gihigugma', 'sa', 'kinabuhi', ',', 'nigradwar', 'siya', 'ug', 'karon', 'usa', 'na', 'ka', 'Magna', 'Cum', 'Laude', 'sa', 'kursong', 'Edukasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0] | cebuaner |
4,877 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DESERVE', ',', 'ZEINAB', '!', 'Trending', 'karon', 'sa', 'mga', 'netizen', 'ang', 'social', 'media', 'post', 'ni', 'Professional', 'Basketball', 'Player', 'Bobby', 'Ray', 'Parks', 'Jr.', 'karong', 'Martes', ',', 'Hunyo', '13', ',', 'gi-feature', 'ang', 'ilang', 'beach', 'photo', 'ni', 'vlogger', 'Zeinab', 'Harake.', 'Sa', 'managlahing', 'mga', 'post', 'sa', 'social', 'media', ',', 'sila', 'si', 'Zeinab', 'ug', 'Bobby', 'Ray', 'dungan', 'nga', 'mipahibalo', 'nga', 'opisyal', 'na', 'sila', 'sa', 'usa', 'ka', 'relasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,878 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Parehong', 'ganahan', 'og', 'sports', 'si', 'Sonny', 'Angara', 'ug', 'ang', 'iyang', 'kamagulangang', 'anak', 'nga', 'si', 'Manolo', ',', 'mao', 'nga', 'karong', 'Mayo', '6', ',', 'iyang', 'gidala', 'ang', 'iyang', 'anak', 'sa', 'Boston', 'aron', 'motan-aw', 'og', 'duwa', 'sa', 'Boston', 'Celtics', 'sa', 'basketball—ang', 'team', 'nga', 'hilig', 'kayo', 'sa', 'senador.', 'Bitbit', 'ang', 'ilang', 'Celtic', 'jersey', ',', 'ang', 'amahan', 'ug', 'anak', 'nagpakita', 'og', 'suporta', 'sa', 'basketball', 'team', 'samtang', 'nagkaon', 'og', 'pizza', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 3, 4, 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 |
4,879 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaambit', 'ni', 'Department', 'of', 'Tourism', '(', 'DOT', ')', '-Western', 'Visayas', 'Director', 'Crisanta', 'Marlene', 'Rodriguez', 'nga', 'ang', 'CPTEx', 'usa', 'ka', 'binuhi', 'nga', 'proyekto', 'ni', 'DOT', 'Secretary', 'Christina', 'Garcia-Frasco', 'aron', 'mapausbaw', 'ang', 'pagbangon', 'sa', 'industriya', 'sa', 'turismo', 'sa', 'nasud', 'human', 'sa', 'pandemya.', 'Magtapok', 'ang', 'DOT', 'ug', 'ang', 'mga', 'stakeholders', 'niini', 'gikan', 'sa', 'unom', 'ka', 'rehiyon', 'sa', 'Iloilo', 'Convention', 'Center', 'alang', 'sa', 'tulo', 'ka', 'adlaw', 'nga', 'travel', 'expo.', 'Tambungan', 'kini', 'sa', 'mga', '2,000', 'ka', 'delegado', 'gikan', 'sa', 'host', 'region', 'sa', 'Western', 'Visayas', ',', 'Central', 'Visayas', ',', 'Eastern', 'Visayas', ',', 'Bicol', 'region', ',', 'Calabarzon', '(', 'Cavite', ',', 'Laguna', ',', 'Batangas', ',', 'Rizal', ',', 'ug', 'Quezon', 'area', ')', ',', 'ug', 'Mimaropa', '(', 'Mindoro', ',', 'Marinduque', ',', 'Romblon', ',', 'ug', 'Mga', 'dapit', 'sa', 'Palawan', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 0, 5, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 6, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0] | cebuaner |
4,880 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Danny', 'Ramirez', ',', '40', ',', 'molupyo', 'sa', 'Sitio', 'Bogo', ',', 'Barangay', 'Curva', ',', 'dakbayan', 'sa', 'Ormoc', ',', 'nipasilong', 'sulod', 'sa', 'kawayan', 'nga', 'shed', 'kay', 'mibundak', 'ang', 'kusog', 'nga', 'uwan', 'dihang', 'naigo', 'sa', 'kilat.', 'Matod', 'ni', 'Kapitan', 'Omar', 'Roel', 'Cartalla', ',', 'hepe', 'sa', 'kapolisan', 'sa', 'Station', '3', ',', 'nasayran', 'sa', 'imbestigasyon', 'nga', 'gigamit', 'ni', 'Ramirez', 'ang', 'iyang', 'cellular', 'phone', 'dihang', 'naigo', 'kini', 'sa', 'kilat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 0, 0, 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, 1, 2, 2, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,881 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'direktang', 'reaksyon', 'ni', 'kanhi', 'senador', 'Manny', 'Pacquiao', 'kon', 'modagan', 'siya', 'pag-usab', 'sa', 'sunod', 'nga', 'Eleksyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,882 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisag', 'layo', 'ka', 'sa', 'akoa', ',', 'gusto', 'ko', 'ma-feel', 'ka', 'nako.', 'Wala'y', 'labaw', ',', 'walay', 'kulang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,883 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GRABE', 'KA', 'LUOY', 'BA', '!', 'Base', 'sa', 'findings', 'sa', 'kapulisan', ',', 'hingpit', 'nga', 'nasunog', 'ang', 'ulo', 'ug', 'bukton', 'ni', 'Tulabing', 'samtang', 'grabeng', 'paso', 'usab', 'sa', 'tiyan', 'niini.', 'Matod', 'sa', 'mga', 'imbestigador', ',', 'nagtuo', 'ang', 'pamilya', 'nga', 'posibleng', 'adunay', 'epilepsy', 'si', 'Tulabing', 'samtang', 'nagluto', 'og', 'kopras', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] | cebuaner |
4,884 | 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', 'komento', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'sa', 'post', 'sa', 'aktres', 'nga', 'si', 'Kakai', 'Bautista', 'sa', 'dihang', 'niingon', 'siya', 'nga', 'dili', 'niya', 'kinahanglan', 'ang', 'lalaki', 'para', 'malipay', ',', 'kay', 'nagkinahanglan', 'siya', 'og', 'kuwarta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,885 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Grabe', 'kaayo', 'akong', 'cravings', '!', 'Dili', 'na', 'pagkaon', ',', 'ang', 'imong', 'atensyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,886 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Basin', 'mao', 'to', 'nga', 'wala', 'ka', 'gipaglaban', ',', 'kay', 'mura', 'kag', 'kalaban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,887 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KA-KUYAW', 'BA', 'ANI', 'UY', '!', 'Gironda', 'sa', '103rd', 'Brigade', 'sa', 'Philippine', 'Army', 'ang', 'gidudahang', 'hide-out', 'ni', 'Islamic', 'State', '(', 'IS', ')', 'extremist', 'leader', 'Commander', 'Abu', 'Zacaria', 'sa', 'Sarimanok', ',', 'Marawi', 'City', 'sayo', 'sa', 'buntag', 'niadtong', 'Miyerkules.', 'Sa', 'maong', 'operasyon', ',', 'napatay', 'si', 'Kumander', 'Abu', 'Zacaria', 'ug', 'ubang', 'mga', 'kauban.', 'Padayon', 'pa', 'ang', 'clearing', 'operation', 'sa', 'militar', 'sa', 'lugar', 'samtang', 'hugot', 'nga', 'gipatuman', 'ang', 'seguridad', 'sulod', 'ug', 'gawas', 'sa', 'Marawi', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 3, 4, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 1, 2, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
4,888 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nunot', 'sa', 'magnitude', '6.3', 'nga', 'linog', 'nga', 'mitay-og', 'sa', 'Calatagan', ',', 'Batangas', ',', 'nga', 'nabatyagan', 'sa', 'mga', 'silingang', 'lugar', 'hangtod', 'sa', 'Metro', 'Manila', 'karong', 'Huwebes', ',', 'Hunyo', '15', ',', 'mahinumdoman', 'ang', 'panawagan', 'ni', 'Senador', 'Sonny', 'Angara', 'sa', 'mga', 'local', 'government', 'units', '(', 'LGUs', ')', 'nga', 'kanunayng', 'andam', 'batok', 'sa', 'mga', 'katalagman', ',', 'sama', 'sa', 'linog', ',', 'bagyo', ',', 'ug', 'uban', 'pang', 'kalamidad.', 'ingon', 'ni', 'Senator', 'Sonny', 'noong', 'Abril', '2019', ',', 'sa', 'panahon', 'nga', 'siya', 'ang', 'chairman', 'sa', 'Senate', 'Committee', 'on', 'Local', 'Government.', 'matud', 'pa', 'niya.', 'Lakip', 'kini', 'sa', 'mga', 'probisyon', 'sa', 'Philippine', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Act', 'of', '2010', '(', 'R.A.', '10121', ')', ',', 'diin', 'gikinahanglan', 'nga', 'igahin', 'ang', '5', '%', 'sa', 'kita', 'sa', 'LGUs', 'ngadto', 'sa', 'Local', 'Disaster', 'Risk', 'Reduction', 'and', 'Management', 'Fund', '(', 'LDRRMF', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 1, 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, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0] | cebuaner |
4,889 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['UYON', 'BA', 'MO', ',', 'BOYS', '?', 'Nakaani', 'og', 'nagkadaiyang', 'reaksyon', 'ang', 'tubag', 'sa', 'mga', 'netizen', 'sa', 'Facebook', 'post', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'karong', 'Miyerkules', ',', 'Hunyo', '14', ',', 'bahin', 'sa', 'iyang', 'post', 'alang', 'sa', 'aktres', 'nga', 'si', 'Andrea', 'Brillantes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,890 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Guard', ',', 'gusto', 'lang', 'nako', 'iyang', 'lambing', '!', '!', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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] | cebuaner |
4,891 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'taho', ',', 'ang', 'biktima', 'natulog', 'uban', 'sa', 'iyang', 'igsuon', 'ug', 'sa', 'iyang', 'asawa', 'dihang', 'gilabayan', 'og', 'granada', 'ang', 'balay', 'sa', 'biktima', 'ug', 'nitugpa', 'sa', 'tiilan', 'niini', 'ug', 'nibuto.', 'Dead-on-the-spot', 'ang', 'biktima', 'human', 'nakaangkog', 'grabeng', 'samad', 'sa', 'ubos', 'nga', 'bahin', 'sa', 'lawas', 'tungod', 'sa', 'shrapnel', 'atol', 'sa', 'pagbuto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,892 | 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', 'pamahayag', 'sa', 'social', 'media', 'personality', 'nga', 'si', 'Rendon', 'Labador', 'sa', 'iyang', 'Facebook', 'post', 'karong', 'Miyerkules', ',', 'Hunyo', '14', ',', 'kalabot', 'sa', 'iyang', 'komento', 'sa', 'aktres', 'nga', 'si', 'Andrea', 'Brillantes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
4,893 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pag-amendar', 'sa', 'PDIC', '(', 'Philippine', 'Deposit', 'Insurance', 'Corporation', ')', 'Charter', 'Act', '(', 'R.A.', '11840', ')', 'sa', 'miaging', 'tuig', ',', 'si', 'Senador', 'Sonny', 'Angara', ',', 'isip', 'sponsor', 'sa', 'maong', 'balaod', ',', 'nipasalig', 'nga', 'maprotektahan', 'ug', 'mas', 'luwas', 'ang', 'kuwarta', 'sa', 'publiko', 'sa', 'ilang', 'mga', 'transaksyon', 'sa', 'bangko.', 'Subay', 'niini', 'nga', 'balaod', ',', 'ang', 'PDIC', 'mopataas', 'sa', 'maximum', 'deposit', 'coverage', 'sa', 'mga', 'depositor', ',', 'mopalig-on', 'sa', 'relasyon', 'tali', 'sa', 'PDIC', 'ug', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', '(', 'BSP', ')', ',', 'ug', 'molugway', 'sa', 'insurance', 'coverage', 'sa', 'mga', 'Islamic', 'banks.', 'Ang', 'PDIC', 'gimanduan', 'sa', 'gobyerno', 'nga', 'tipigan', 'ang', 'kuwarta', 'sa', 'publiko', 'sa', 'mga', 'bangko', 'nga', 'protektado', 'sa', 'bisan', 'unsang', 'krisis', ',', 'lakip', 'ang', 'pagsira', 'sa', 'bangko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 4, 4, 4, 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 |
4,894 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Cebu', 'City', 'Police', 'Office', '(', 'CCPO', ')', 'niingon', 'nga', 'ang', 'mga', 'nasakmit', 'nga', 'ilegal', 'nga', 'drugas', 'gikan', 'sa', 'Enero', 'hangtod', 'Mayo', 'niabot', 'sa', '23.45', 'kilos', 'nga', 'shabu', 'nga', 'nagkantidad', 'og', 'P159', 'milyones', ',', 'itandi', 'sa', 'samang', 'panahon', 'sa', 'miaging', 'tuig.', 'Sa', 'samang', 'panahon', 'sa', 'miaging', 'tuig', ',', 'ang', 'CCPO', 'nihimo', 'og', '566', 'ka', 'anti-drug', 'operations', 'ug', 'nasakmit', 'ang', '15.48', 'kilos', 'nga', 'shabu', 'nga', 'mobalor', 'og', 'P107.7', 'milyones', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,895 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Barangayan', 'gihimo', 'sa', 'Sugbo', 'aron', 'mas', 'makapangandam', 'sa', 'sunod', 'tuig', 'nga', 'Sinulog.', 'Matod', 'ni', 'Cebu', 'City', 'Mayor', 'Michael', 'Rama', 'nga', 'adunay', 'laing', 'susamang', 'kalihokan', 'karong', 'Nobiyembre.', 'Nagsugod', 'ang', 'Barangayan', 'duol', 'sa', 'Fuente', 'Osmeña', 'Circle', 'alang', 'sa', 'street', 'dancing', ',', 'ug', 'natapos', 'sa', 'Plaza', 'Independencia', 'alang', 'sa', 'ritual', 'showdown', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 7, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0] | cebuaner |
4,896 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Na-', 'laugh', 'trip', 'ang', 'netizen', 'kay', 'Juliane', 'Sanchez', 'Obradorll', 'sa', 'Cagayan', 'de', 'Oro', 'human', 'niya', 'gi-post', 'ang', 'iyang', 'kasagmuyo', 'sa', 'iyang', 'national', 'ID.', '“Maypag', 'nasunog', 'nalang', 'kang', 'IDha', 'ka', 'abi', 'nimug', 'gamiton', 'taka', '?', 'Thank', 'you', 'nalang', 'maypag', 'wanalang', 'ka', 'ni', 'abot', 'wako', 'nag', 'basi', 'sa', 'nawong', 'ha', 'ky', 'diman', 'kaayu', 'ta', 'hitsuraan', 'nag', 'basi', 'kos', 'buhok', 'nganong', 'nawala”', 'sa', 'iyang', 'Facebook', 'post.', 'Ang', 'iyang', 'post', 'niabot', 'na', 'sa', '29,000', 'reactions', 'ug', '26,000', 'shares', 'ug', '3.5K', 'comments', 'niini.', 'Nitiyabaw', 'ang', 'mga', 'netizen', ':', '“Bantog', 'dugay', 'ni', 'abot', 'ky', 'gi', 'agi', 'pag', 'chemo”', 'saad', 'sa', 'usa.', '“Kaya', 'ka', 'napanot', 'kasi', 'nasunog', 'ang', 'Manila', 'Post', 'Office.”', 'Hirit', 'pa', 'sa', 'usa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
4,897 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mipasalamat', 'kang', 'House', 'Speaker', 'Martin', 'Romualdez', 'ang', 'independent', 'minority', 'congressman', 'ug', 'Liberal', 'Party', '(', 'LP', ')', 'President', ',', 'Albay', '1st', 'District', 'Rep.', 'Edcel', 'Lagman', 'human', 'gitabangan', 'dayon', 'sa', 'House', 'Lider', 'ang', 'libu-libo', 'ka', 'mga', 'Albaniano', 'nga', 'mibakwit', 'gikan', 'sa', 'pag-aaburoto', 'sa', 'Bulkang', 'Mayon.', 'Gawas', 'kang', 'Speaker', 'Romualdez', ',', 'gipasalamatan', 'usab', 'ni', 'Lagman', 'si', 'Department', 'of', 'Social', 'Welfare', 'and', 'Development', '(', 'DSWD', ')', 'Secretary', 'Rex', 'Gatchalian.', 'ingon', 'ni', 'Lagman.', 'Ang', 'distrito', 'sa', 'beteranong', 'magbabalaod', 'nalakip', 'sa', 'tulo', 'ka', 'distrito', 'sa', 'Albay', 'nga', 'matabangan', 'sa', 'kinatibuk-ang', 'P33-million', 'nga', 'hinabang', 'gikan', 'sa', 'tanggapan', 'ni', 'Speaker', 'Romualdez', 'ug', 'sa', 'DSWD.', 'Ang', 'mga', 'bakwit', 'sa', 'Mayon', 'makadawat', 'og', 'P1', 'milyon', '(', 'P500,000', 'cash', 'ug', 'P500,000', 'nga', 'balor', 'sa', 'relief', 'packs', ')', 'gikan', 'sa', 'Office', 'of', 'the', 'Speaker', ',', 'dugang', 'sa', 'P10', 'milyones', 'nga', 'payouts', 'gikan', 'sa', 'Assistance', 'to', 'Individuals', 'in', 'Crisis', 'Situations', '(', 'AICS', ')', 'program', 'sa', 'DSWD', 'alang', 'sa', 'matag', 'usa', ',', 'sa', 'tulo', 'ka', 'distrito', 'sa', 'Albay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 5, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 0, 0, 1, 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, 0, 0, 3, 0, 0, 0, 0, 5, 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, 7, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
4,898 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giganahan', 'ang', 'mga', 'netizen', 'sa', 'Instagram', 'post', 'ni', 'Actress', 'ug', 'ang', 'unang', 'Transgender', 'News', 'Anchor', 'sa', 'Frontline', 'Pilipinas', ',', 'KaladKaren', ',', 'karong', 'Lunes', ',', 'Hunyo', '12', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
4,899 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Namulong', 'sa', 'kapin', 'sa', '1,000', 'ka', 'mga', 'propesyonal', 'nga', 'tigpamuhunan', 'ug', 'mga', 'eksperto', 'sa', 'industriya', 'nga', 'mitambong', 'sa', 'Nomura', 'Investment', 'Forum', 'Asia', 'sa', 'Singapore', 'niadtong', 'Hunyo', '7', ',', 'si', 'Atty.', 'Si', 'Mike', 'Toledo', ',', 'direktor', 'sa', 'Government', 'Relations', 'and', 'Public', 'Affairs', 'ug', 'Manuel', 'V.', 'Pangilinan-led', 'Metro', 'Pacific', 'Investments', 'Corporation', '(', 'MPIC', ')', ',', 'nagpasiugda', 'sa', 'praktikal', 'nga', 'pamaagi', 'sa', 'gobyerno', 'sa', 'Pilipinas', 'sa', 'dugang', 'nga', 'pagdasig', 'sa', 'pribadong', 'sektor', 'nga', 'pamuhunan.', 'matud', 'ni', 'Toledo.', 'dugang', 'niya.', 'Ang', 'BOT', 'Law', 'nagtanyag', 'sa', 'legal', 'nga', 'framework', 'para', 'sa', 'mga', 'ahensya', 'sa', 'gobyerno', 'nga', 'mosulod', 'sa', 'mga', 'kontrata', 'sa', 'PPP', 'uban', 'sa', 'mga', 'kuwalipikadong', 'pribadong', 'sektor', 'nga', 'mamumuhunan', 'alang', 'sa', 'pagkompleto', 'sa', 'imprastraktura', 'sa', 'gobyerno', 'o', 'mga', 'proyekto', 'sa', 'kalamboan.', 'Atol', 'sa', 'tinuig', 'nga', 'komperensya', ',', 'gibutyag', 'usab', 'ni', 'Toledo', 'nga', 'plano', 'sa', 'administrasyong', 'Marcos', 'nga', 'molusad', 'og', '194', 'ka', 'flagship', 'projects', 'nga', 'mobalor', 'og', 'P9', 'trilyon.', 'Ang', 'MPIC', 'mao', 'ang', 'nag-unang', 'infrastructure', 'conglomerate', 'luyo', 'sa', 'pinakataas', 'nga', 'taytayan', 'sa', 'nasod', 'Cebu-Cordova', 'Link', 'Expressway', '(', 'CCLEX', ')', ',', 'ang', 'Cavite-Laguna', 'Expressway', '(', 'CALAX', ')', ',', 'ang', 'NLEX', 'connector', 'road', ',', 'ug', 'ang', 'Subic-Clark', 'Tarlac', 'Expressway', '(', 'SCTEX', ')', ',', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 7, 8, 8, 8, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 2, 2, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 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, 1, 0, 0, 0, 0, 1, 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, 5, 6, 6, 6, 6, 6, 0, 0, 5, 6, 6, 6, 6, 0, 0, 5, 6, 6, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
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