Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,800 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'na', 'sa', 'ilang', 'gitan-aw', 'karon', 'nga', 'himuong', 'participante', 'niini', 'ang', 'mga', 'millenials', 'o', 'batan-on', 'nga', 'ganahan', 'ug', 'adventure', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,801 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'heritage', 'tour', 'sa', 'nabantog', 'nga', 'mga', 'karaang', 'simbahan', 'ug', 'edipisyo', 'sa', 'Southern', 'Cebu', ',', 'apil', 'na', 'sa', 'itinerary', 'ang', 'canyoneering', ',', 'whale', 'shark', 'watching', 'ug', 'pagdu-aw', 'sa', 'falls', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,802 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitakda', 'ang', 'Suroy-suroy', 'sa', 'Enero', '24', ',', '25', 'ug', '26.', 'Nagsugod', 'na', 'og', 'dawat', 'ang', 'PTO', 'ug', 'mga', 'inquiries', 'ug', 'reservation', 'alang', 'sa', 'kalihukan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,803 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'pa', 'hinuon', 'matumbok', 'kon', 'tagpila', 'ang', 'package', 'alang', 'niini', 'kay', 'nagpadayon', 'pa', 'sila', 'pagsubay', 'sa', 'costing', 'sa', 'room', 'accommodation', 'sa', 'mga', 'resort', 'nga', 'ilang', 'abtan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,804 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidapit', 'ang', 'mga', 'interesado', 'sa', 'pag-book', 'o', 'pagpanguta', 'alang', 'sa', 'dugang', 'detalye', 'sa', 'PTO', 'sa', 'Kapitolyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0] | cebuaner |
5,805 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MOABOT', 'sa', '800', 'ngadto', 'sa', '1,000', 'ka', 'mga', 'trabahante', 'ang', 'gikinahanglan', 'sa', 'pagtukod', 'sa', 'ikatulong', 'taytayan', 'sa', 'Sugbo', 'o', 'Cebu-Cordova', 'Link', 'Expressway', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 6, 0] | cebuaner |
5,806 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apil', 'na', 'niini', 'ang', 'mga', 'trabahante', 'gikan', 'sa', 'ubang', 'mga', 'nasod', 'ug', 'mga', 'mamumuo', 'sa', 'DM', 'Consunji', 'Inc.', ',', 'ug', 'First', 'Balfour', 'Inc', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0] | cebuaner |
5,807 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'gihatagan', 'sa', 'P22.6', 'bilyones', 'nga', 'design', 'ug', 'build', 'contract', 'sa', 'Metro', 'Pacific', 'Tollways', 'Corp.', 'niadtong', 'Huwebes', 'atol', 'sa', 'kalihokan', 'sa', 'Waterfront', 'Hotel', 'and', 'Casino', ',', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
5,808 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Rodrigo', 'Franco', ',', 'presidente', 'ug', 'chief', 'executive', 'officer', 'sa', 'MPTC', ',', 'niingon', 'ang', 'pagkuha', 'og', 'mga', 'trabahante', 'dili', 'lang', 'kutob', 'sa', 'konstruksyon', 'sanglit', 'mag-hire', 'usab', 'sila', 'og', 'mga', 'engineer', 'ug', 'office', 'staff', 'sa', 'pagdumala', ',', 'pag-operate', 'ug', 'pag-maintain', 'sa', 'pasilidad', 'sa', 'higayon', 'mahuman', 'ang', 'pagtukod', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,809 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'ni', 'Camba', ',', 'ang', 'proyekto', 'makatabang', 'sa', 'ubang', 'patigayon', 'sama', 'sa', 'suppliers', ',', 'subcontractors', 'ug', 'ubang', 'negosyo', 'nga', 'malambigit', 'sa', 'konstruksyon', 'ug', 'operation', 'ug', 'maintenance', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,810 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Franco', 'niingon', 'pahibaanan', 'sa', 'mga', 'sakyanan', 'nga', 'moagi', 'sa', 'CCLEX', 'kay', 'magsugod', 'sa', 'P89', 'ug', 'mag-agad', 'sa', 'klase', 'ug', 'gibug-aton.', 'Ang', '8.5', 'kilometros', 'nga', 'CCLEX', 'maoy', 'unang', 'toll', 'road', 'expansion', 'sa', 'Visayas', 'ug', 'Mindanao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0] | cebuaner |
5,811 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'PCInsp.', 'Mercy', 'Villaro', 'nga', 'kuyog', 'ni', 'Obiña', 'atol', 'sa', 'insidente', 'ang', 'iyang', '9', 'anyos', 'nga', 'anak', 'nga', 'lalake', 'atol', 'sa', 'aksidente', 'ala', '1:30', 'sa', 'kaadlawon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,812 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'ang', 'mag-amahan', 'nabangga', 'sa', 'Isuzu', 'dump', 'truck', 'kinsa', 'gidrayban', 'ni', 'Rodel', 'Cometa', ',', '39', ',', 'taga', 'Tabugon', ',', 'Cebu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 0, 5, 0] | cebuaner |
5,813 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daling', 'gidala', 'sa', 'tambalanan', 'ang', 'mag-amahan', 'apan', 'wa', 'na', 'matabang', 'sa', 'doktor', 'ang', 'amahan', 'nga', 'nagrabihan', 'nunot', 'sa', 'kakusog', 'sa', 'impact', 'sa', 'aksidente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,814 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kasamtangang', 'nagpaalim', 'sa', 'tambalanan', 'ang', 'anak', 'ni', 'Obiña', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0] | cebuaner |
5,815 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'laing', 'bahin', ',', 'kritikal', 'sab', 'ang', 'usa', 'ka', '38', 'anyos', 'nga', 'lalake', 'human', 'nga', 'kini', 'maligsi', 'og', 'dumptruck', 'sa', 'dihang', 'kalit', 'milabang', 'sa', 'Avenue', 'gahapon', 'sa', 'kaadlawon.', '(', 'JPP', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,816 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'report', 'sa', 'Police', 'Regional', 'Office', '(', 'PRO', ')', '8', ',', 'dunay', '30', 'ka', 'armadong', 'tawo', 'nga', 'gituhoang', 'sakop', 'sa', 'Communist', 'Terrorist', 'Extortionist', '-', 'New', 'Peoples', 'Army', '(', 'CTE-NPA', ')', 'ang', 'mikompiska', 'sa', 'mga', 'butang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0] | cebuaner |
5,817 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'mga', 'cellphone', 'ug', 'pitaka', ',', 'duna', 'usa', 'gikuha', 'nga', 'mugbo', 'nga', 'armas', 'ang', 'maong', 'grupo', 'gikan', 'kang', 'Alex', 'Omanito', 'ug', 'Ervin', 'Pinca', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0] | cebuaner |
5,818 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'makapalag', 'ang', 'mga', 'trabahante', 'ug', 'ang', 'duha', 'ka', 'tag-iya', 'sa', 'armas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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 |
5,819 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'pagpanguha', 'sa', 'mga', 'gamit', ',', 'misibat', 'padung', 'sa', 'interior', 'portion', 'sa', 'maong', 'barangay', 'ang', 'grupo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,820 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Patay', 'ang', '70-anyos', 'nga', 'babaye', 'human', 'aksidenteng', 'nadasmagan', 'og', 'motorsiklo', 'niadtong', 'Lunes', 'sa', 'buntag', 'didto', 'sa', 'Brgy.', 'Bagacay', ',', 'lungsod', 'sa', 'Talibon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
5,821 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'ang', 'biktima', 'nga', 'si', 'Catalina', 'Torremocha', ',', '70', ',', 'minyo', 'ug', 'residente', 'sa', 'lugar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0] | cebuaner |
5,822 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'suspek', 'giila', 'nga', 'si', 'Joseph', 'Cajes', ',', '34', ',', 'minyo', ',', 'messenger', 'sa', 'usa', 'ka', 'forwarding', 'company', 'ug', 'residente', 'sa.', 'Brgy.', 'Sto', 'Nino', ',', 'lungsod', 'sa', 'Talibon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
5,823 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Cajes', 'ang', 'gitumbok', 'nga', 'drayber', 'sa', 'motorsiklo', 'nga', 'nakadasmag', 'sa', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,824 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakisusi', 'sa', 'kapulisan', ',', 'nasayran', 'nga', 'ang', 'suspek', 'nagdagan', 'sakay', 'sa', 'iyang', 'motorsiklo', 'gikan', 'sa', 'Talibon', 'padung', 'sa', 'Getafe', 'alang', 'sa', 'paghatod', 'og', 'package', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
5,825 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matud', 'ni', 'Provincial', 'Schools', 'Division', 'Superintendent', 'Wilfreda', 'Bongalos', 'nga', 'gisusi', 'og', 'maayo', 'sa', 'DepEd', 'ang', 'ilang', 'records', 'ug', 'natinong', 'way', 'estudyante', 'sa', 'Bohol', 'ang', 'nahatagan', 'sa', 'maong', 'immunization', 'program', 'sa', 'gobyerno', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,826 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidugang', 'ni', 'Bongalos', 'nga', 'DOH', 'nakig-koordinar', 'kaniadto', 'sa', 'DepEd-Bohol', 'alang', 'unta', 'sa', 'maong', 'anti-dengue', 'vaccination', 'campaign', 'apan', 'wa', 'pa', 'niabot', 'ang', 'Dengvaxia', 'nga', 'mga', 'bakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,827 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Provincial', 'Health', 'Officer', 'Reymoses', 'Cabagnot', 'nikumpirmar', 'nga', 'way', 'niabot', 'niini', 'sa', 'Bohol', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 0] | cebuaner |
5,828 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'pasahero', 'ang', 'stranded', 'human', 'wa', 'motugot', 'ang', 'Coast', 'Guard', 'sa', 'pagpabiyahe', 'sa', 'mga', 'sea', 'vessels', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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 |
5,829 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'lugar', 'ang', 'gibahaan', 'ug', 'nag-landslide', 'tungod', 'sa', 'way', 'hunong', 'nga', 'uwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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 |
5,830 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisuspenso', 'usab', 'ang', 'mga', 'klase', 'sa', 'mga', 'tunghaan', 'nga', 'apektado', 'sa', 'bagyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,831 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Biliran', ',', 'nagdeklara', 'usab', 'og', 'suspension', 'sa', 'trabaho', 'sa', 'gobyerno', 'gawas', 'lang', 'niadtong', 'dunay', 'dakong', 'papel', 'sa', 'kalamidad', 'sama', 'sa', 'DRRMC', ',', 'RHU', 'ug', 'DSWD', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0] | cebuaner |
5,832 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'di', 'mominos', '25', 'ka', 'mga', 'barko', 'ug', 'fastcraft', 'ang', 'kanselado', 'ang', 'mga', 'biyahe', 'gumikan', 'sa', 'dautang', 'panahon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,833 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'delikado', 'kini', 'sa', 'pagbaha', 'ug', 'flashfloods', 'labi', 'na', 'ang', 'kalit', 'nga', 'pag-awas', 'sa', 'tubig', 'sa', 'kasapaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,834 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Coast', 'Guard', 'Station', 'Cebu', 'Commander', 'Jerome', 'Cayabyab', 'niingon', 'kasagaran', 'sa', 'mga', 'biyahe', 'nga', 'nakansilar', 'kay', 'paingon', 'o', 'gikan', 'sa', 'Ormoc', ',', 'Maasin', ',', 'Masbate', ',', 'isla', 'sa', 'Camotes', ',', 'Bantayan', 'ug', 'ubang', 'bahin', 'sa', 'Leyte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 0, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 5, 0] | cebuaner |
5,835 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'PDRRMO', 'nga', 'anaa', 'karon', 'sa', 'orange', 'alert', ',', 'niandam', 'na', 'sa', 'ilang', 'mga', 'kahimanan', 'aron', 'dali', 'sila', 'nga', 'maka-responde', 'sa', 'mga', 'dapit', 'nga', 'makasinati', 'ug', 'kakulian', 'tungod', 'sa', 'bagyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,836 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'sa', 'ilang', 'monitoring', ',', 'moderate', 'rains', 'lang', 'ang', 'nasinati', 'sa', 'kalungsuran', 'lukop', 'lalawigan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,837 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', ',', 'gihimo', 'ang', 'ceremonial', 'nga', 'paghatag', 'sa', 'Notice', 'of', 'Award', 'sa', 'P22.6', 'bilyones', 'nga', 'kontrata', 'alang', 'sa', 'pagtukod', 'sa', 'Cebu-Cordova', 'Link', 'Expressway', 'nga', 'sugdan', 'sa', 'ikaduhang', 'quarter', 'sa', '2018', 'ug', 'gipaabot', 'mahuman', 'sa', '2021', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,838 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kalihokan', 'gisaksihan', 'ni', 'Cebu', 'City', 'Mayor', 'Tomas', 'Osmeña', ',', 'Sitoy', ',', 'ug', 'anak', 'niini', 'nga', 'si', 'Cordova', 'Mayor', 'Mary', 'Therese', 'Sitoy-Cho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0, 1, 2, 2, 0] | cebuaner |
5,839 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'presidential', 'adviser', 'nga', '27', 'ka', 'tuig', 'na', 'niya', 'nga', 'gidamgo', 'ang', 'ikatulong', 'taytayan', 'niadtong', 'panahon', 'dihang', 'giplanohan', 'ang', 'pagtukod', 'sa', 'Marcelo', 'Fernan', 'Bridge', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 7, 8, 8, 0] | cebuaner |
5,840 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Sitoy', 'nipaambit', 'nagsugod', 'kini', 'dihang', 'nakadawat', 'sila', 'og', '“unsolicited', 'offer”', 'gikan', 'sa', 'MPTC', 'niadtong', 'Agusto', '2014', 'dihang', 'siya', 'pa', 'ang', 'mayor', 'sa', 'Cordova', 'nga', 'magtukod', 'kini', 'og', 'taytayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
5,841 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Napasayon', 'ang', 'proseso', 'gumikan', 'sa', 'ordinansa', 'sa', 'lungsod', 'bahin', 'sa', 'PPP', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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 |
5,842 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nisugilon', 'nga', 'nilahos', 'sila', 'kang', 'Presidente', 'Rodrigo', 'Duterte', 'pagpahibawo', 'sa', 'proyekto', 'ug', 'wala', 'na', 'moagi', 'sa', 'Neda', ',', 'DPWH', 'ug', 'RDC', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 0, 3, 0, 3, 0] | cebuaner |
5,843 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipusil', 'patay', 'sa', 'iyang', 'uyab', 'ang', '24', 'anyos', 'nga', 'babaye', 'tungod', 'sa', 'selos', 'sud', 'sa', 'lodging', 'house', 'diin', 'sila', 'nag-check-in', 'kagahapon', 'sa', 'kaadlawon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,844 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kapulisan', 'nitumbok', 'sa', 'suspek', 'nga', 'nalambigit', 'sa', 'drugas', 'ug', 'kanunay', 'magselos', 'sa', 'babaye', ',', 'kinsa', 'gusto', 'na', 'untang', 'makigbuwag', 'kaniya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,845 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'didto', 'na', 'ang', 'duha', 'sa', 'Room', '14', ',', 'nikanaug', 'pagbalik', 'si', 'Cabuenas', 'tungod', 'kay', 'gipadaplin', 'ang', 'motorsiklo', 'nga', 'iyang', 'gisakyan', 'nga', 'nakaali', 'sa', 'agianan', 'sa', 'ubos', 'nga', 'bahin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,846 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'pagsaka', 'og', 'balik', ',', 'nadungong', 'sa', 'mga', 'staff', 'ang', 'duha', 'ka', 'sunod-sunod', 'nga', 'mga', 'buto', 'sa', 'armas', 'ug', 'dayon', 'nigawas', 'si', 'Parba', 'base', 'sa', 'kuha', 'sa', 'close', 'circuit', 'televison', 'camera', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,847 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kapulisan', 'sa', 'Talisay', 'nakakuha', 'og', 'usa', 'ka', 'slug', 'sa', 'bala', 'nga', 'wala', 'pa', 'matino', 'ang', 'kalibre', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,848 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'cashier', 'kinsa', 'nihangyo', 'nga', 'di', 'lang', 'magpaila', 'niingon', 'nga', 'suki', 'na', 'nila', 'ang', 'duha', 'ug', 'kanunay', 'usab', 'kini', 'nilang', 'madunggan', 'nga', 'mag-away', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,849 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'tungod', 'kay', 'gusto', 'nang', 'buwagan', 'ni', 'Cabuenas', 'si', 'Parba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 1, 0] | cebuaner |
5,850 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Salud', 'Juarez', ',', 'apohan', 'ni', 'Cabuenas', ',', 'niingon', 'nga', 'minyo', 'usab', 'ang', 'iyang', 'apo', 'apan', 'buwag', 'sa', 'bana', 'ug', 'aduna', 'silay', 'duha', 'ka', 'mga', 'anak', 'nga', 'atoa', 'sa', 'Mindanao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 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 |
5,851 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Way', 'trabaho', 'ang', 'iyang', 'apo', 'ug', 'giingong', 'panagsa', 'ra', 'usab', 'moadto', 'sa', 'ilang', 'balay', 'sanglit', 'nag-ipon', 'na', 'kini', 'ug', 'si', 'Parba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 0] | cebuaner |
5,852 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikompirmar', 'usab', 'sa', 'mga', 'paryenti', 'ni', 'Cabuenas', 'nga', 'kanunay', 'mag-away', 'ang', 'duha', 'tungod', 'gihapon', 'sa', 'selos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,853 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Chief', 'Insp.', 'Mercy', 'Villaro', ',', 'tigpamaba', 'sa', 'Mandaue', 'City', 'Police', 'Office', '(', 'MCPO', ')', ',', 'niingon', 'nga', 'ila', 'nang', 'nakit-an', 'ang', 'duha', 'ka', 'mga', 'rekruter', ',', 'apan', 'di', 'pa', 'nila', 'madakop', 'gilayon', 'kay', 'duha', 'na', 'ka', 'mga', 'buwan', 'ang', 'hitabo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 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] | cebuaner |
5,854 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Maghuwat', 'una', 'sila', 'sa', 'arrest', 'warrant', 'nga', 'luwatan', 'sa', 'korte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,855 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Villaro', 'nga', 'ang', 'duha', 'ka', 'mga', 'dalagita', 'nangabiktima', 'sa', 'human', 'trafficking', 'niadtong', 'Oktubre', '1', 'ug', 'Oktubre', '10', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0] | cebuaner |
5,856 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'biktima', ',', 'kinsa', 'pulos', 'naa', 'sa', 'grade', '8', ',', 'gibugawan', 'sa', 'duha', 'ka', 'mga', 'tinun-an', ',', 'usa', 'niini', 'ang', '16', 'anyos', 'nga', 'schoolmate', 'sa', 'duha', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,857 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Laing', 'rekruter', 'nga', 'giila', 'lang', 'nga', 'si', 'MJ', 'Jordan', ',', 'taga', 'Brgy.', 'Talamban', ',', 'dakbayan', 'sa', 'Sugbo', 'ang', 'ning-contact', 'kang', 'Tan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 6, 0, 0, 0, 5, 0, 0, 0, 1, 0] | cebuaner |
5,858 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpakisusi', 'sila', 'sa', 'kapulisan', 'kabahin', 'sa', 'background', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,859 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Garry', 'B.', 'Lao', ',', 'executive', 'director', 'sa', 'COSAP', ',', 'nagkanayon', 'nga', 'ang', 'drama', 'mas', 'makatabang', 'sa', 'pagpasabot', 'sa', 'publiko', 'kung', 'unsa', 'ang', 'mga', 'posibleng', 'dautang', 'resulta', 'sa', 'paggamit', 'sa', 'gidili', 'nga', 'druga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 2, 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 |
5,860 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'drama', 'matang', 'sa', 'komunikasyon', 'sa', 'katawhan', 'nga', 'importrane', 'para', 'sa', 'edukasyon', 'ug', 'pagsulbad', 'sa', 'problema', 'sa', 'pag-abuso', 'sa', 'druga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,861 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'COSAP', 'dunay', 'drama', 'kaniadto', 'ubos', 'sa', 'pagduso', 'ni', 'Cebu', 'City', 'Mayor', 'Tomas', 'Osmena', 'apan', 'nahunong', 'kini', 'sa', 'paglingkod', 'sa', 'laing', 'administrasyon', 'sa', 'dakbayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,862 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karon', 'banhawon', 'kini', 'sa', 'COSAP', 'ug', 'mas', 'paninduton', 'human', 'ang', 'mga', 'materyales', 'sa', 'pagmugna', 'sa', 'drama', 'lakip', 'na', 'ang', 'scripts', ',', 'hashasan', 'pag-ayo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,863 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'presentasyon', 'pagahimuon', 'sa', 'mga', 'piyesta', 'sa', 'mga', 'baranggay', 'ug', 'ubang', 'okasyon', 'nga', 'magkatapok', 'ang', 'daghang', 'tawo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,864 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakigestorya', 'na', 'sab', 'si', 'Lao', 'ngadto', 'ni', 'Alan', 'Nacorda', 'nga', 'mamahimo', 'siya', 'isip', 'drama', 'director', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,865 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagdasig', 'sab', 'si', 'Lao', 'sa', 'mga', 'kabantanonan', 'sa', 'kada', 'barangay', 'nga', 'moboluntaryo', 'sa', 'maong', 'drama', 'aron', 'dali', 'ang', 'pag', 'pull-out', 'ug', 'maapil', 'sila', 'sa', 'presentasyon', 'ngadto', 'sa', 'ilang', 'barangay', 'para', 'adunay', 'dakong', 'partisipasyon', 'sa', 'komunidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [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] | cebuaner |
5,866 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Limitahan', 'ang', 'mga', 'moapil', 'sa', 'drama', 'aron', 'sab', 'malikayan', 'nila', 'ang', 'kasamok', 'samtang', 'ipahigayon', 'ang', 'mga', 'presentasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,867 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitandi', 'sab', 'ni', 'Lao', 'ang', 'nagkalainlaing', 'modulo', 'sa', 'henerasyon', 'karon', 'nga', 'mao', 'ang', 'paggahin', 'og', 'dakong', 'oras', 'sa', 'internet', 'ug', 'social', 'media', 'sa', 'kabatanonan', 'kay', 'sa', 'kalihokan', 'sama', 'niini', 'nga', 'mas', 'makahatag', 'kanila', 'og', 'pagtulonan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,868 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Lao', 'mohangyo', 'sab', 'ngadto', 'ni', 'Councilor', 'Dave', 'Tumulak', 'nga', 'mo-sponsor', 'niini', 'alang', 'sa', 'suporta', 'sa', 'konseho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,869 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Zafra', 'niingon', 'nga', 'mas', 'labing', 'maayo', 'sab', 'unta', 'kung', 'mahimong', 'musical', 'play', 'kini', 'para', 'mas', 'ma-enganyo', 'ang', 'kabantanonan', 'apan', 'andam', 'sab', 'siya', 'nga', 'motabang', 'para', 'mahinayon', 'ang', 'COSAP', 'Drama', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 3, 0, 0] | cebuaner |
5,870 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibalo', 'sa', 'Cebu', 'South', 'Bus', 'Terminal', '(', 'CSBT', ')', 'nga', 'duna', 'silay', 'bag-ong', 'schedule', 'sa', 'biyahe', 'sa', 'mga', 'bus', 'alang', 'sa', 'holiday', 'season', 'ug', 'ipatuman', 'kini', 'sa', 'sunod', 'semana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,871 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'CSBT', 'security', 'manager', 'Joy', 'Tumulak', 'miingon', 'nga', 'sugod', 'sa', 'Disyembre', '22', 'hangtod', 'sa', 'Disyembre', '24', ',', 'ala', '1', 'na', 'sa', 'kaadlawon', 'ang', 'labing', 'unang', 'biyahe', 'sa', 'Ceres', 'Bus', 'ug', 'alas', '10', 'lang', 'sa', 'gabii', 'ang', 'katapusang', 'pagbiya', 'niini', 'diha', 'sa', 'terminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 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] | cebuaner |
5,872 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'mga', 'mini-bus', ',', 'alas', '4', 'sa', 'kaadlawon', 'ang', 'labing', 'unang', 'biyahe', 'ug', 'alas', '8:00', 'sa', 'gabii', 'ang', 'katapusan', 'sulod', 'sa', 'maong', 'mga', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,873 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gituyo', 'sa', 'CSBT', 'management', 'ang', 'pagbutang', 'ug', 'tataw', 'nga', 'schedule', 'sa', 'mga', 'biyahe', 'aron', 'dili', 'maghuot', 'diha', 'sa', 'terminal', 'ang', 'mga', 'tawo', 'ug', 'malikayan', 'nga', 'magsalig', 'ang', 'mga', 'pasahero', 'ug', 'magpagabii', 'na', 'ug', 'maayo', 'sa', 'biyahe', 'tungod', 'sa', 'daghang', 'kasakyan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,874 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'miaging', 'kalag-kalag', ',', 'nipatuman', 'ug', '24', 'oras', 'nga', 'biyahe', 'sa', 'mga', 'bus', 'diha', 'sa', 'terminal', 'apan', 'matod', 'ni', 'Tumulak', 'nga', 'karong', 'holiday', 'season', ',', 'angayang', 'planuhon', 'nag', 'tarong', 'sa', 'mga', 'biyahedor', 'ang', 'ilang', 'oras', 'aron', 'makasakay', 'dayon', 'paingon', 'sa', 'ilang', 'destinasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,875 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'did-an', 'sa', 'CSBT', 'management', 'ang', 'mga', 'pasahero', 'nga', 'mangatulog', 'o', 'maghuwat', 'diha', 'sa', 'terminal', 'ang', 'mga', 'pasahero', 'nga', 'dili', 'kasakay', 'sa', 'katapusang', 'biyahe', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,876 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'ni', 'Tumulak', 'ang', 'publiko', 'nga', 'dili', 'moahat', 'og', 'adto', 'sa', 'terminal', 'kon', 'apiki', 'na', 'sa', 'oras', 'ug', 'dili', 'makaabot', 'sa', 'last', 'trip', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,877 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Rubio', 'nga', 'dali', 'siyang', 'nirekomendar', 'nga', 'relibuhan', 'ang', 'maong', 'JO1', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,878 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'nga', 'nagtiner', 'og', 'armas', 'nga', 'walay', 'lisensya', 'sa', 'isla', 'sa', 'Camotes', 'ang', 'nasikop', 'sa', 'ronda', 'sa', 'mga', 'sakop', 'sa', 'Criminal', 'Investigation', 'and', 'Detection', 'Group', '7', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 4, 0] | cebuaner |
5,879 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Armado', 'og', 'search', 'warrants', ',', 'ang', 'CIDG-7', 'unang', 'nironda', 'ug', 'nasikop', 'si', 'Allan', 'Otadoy', ',', '47', ',', 'sa', 'Brangay', 'Cagcagan', ',', 'Poro', 'nga', 'nakuhaan', 'og', 'kalibre', '45', 'ug', 'mga', 'bala', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,880 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niatubang', 'sa', 'Senate', 'blue', 'ribbon', 'committee', 'nga', 'nagsusi', 'sa', 'kontrobersiyal', 'nga', 'P3.5-billion', 'program', ',', 'si', 'Aquino', 'niingong', 'nga', 'walay', 'niprotesta', 'sa', 'iyang', 'hukom', 'niadto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,881 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Cebu', 'apil', 'sa', 'gipatuman', 'ang', 'programa', 'apan', 'wala', 'gisulti', 'ni', 'Noynoy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,882 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Aquino', 'niingon', 'nga', 'ang', 'iyang', 'administrasyon', 'nagsugod', 'sa', 'pagtutok', 'sa', 'dengue', 'human', 'nakadawat', 'og', ''memo', ''', 'gikan', 'ni', 'kanhi', 'Health', 'Secretary', 'Enrique', 'Ona', 'niadtong', '2010', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 6, 0, 0, 0] | cebuaner |
5,883 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', ',', 'ang', 'memo', 'nagsaysay', 'nga', 'dunay', 'lima', 'ka', 'rehiyon', 'nga', ''alarming', 'ang', 'pagtaas', ''', 'sa', 'kaso', 'sa', 'dengue', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,884 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', 'nga', 'dul-an', 'sa', '2.8', 'milyon', 'ka', 'awo', 'ang', 'risgo', 'sa', 'naasoy', 'nga', 'sakit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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 |
5,885 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'walay', 'gibuhat', ',', 'dason', 'niya', ',', 'dako', 'usab', 'nga', 'tulubagon', 'niya', 'gikan', 'sa', 'mga', 'kritiko', 'sa', 'lahi', 'nga', 'isyu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,886 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Aquino', 'niangkon', 'nga', 'niadto', 'sa', 'Paris', 'sa', '2015', 'alang', 'sa', 'COP21', 'conference', ',', 'ug', 'sa', 'sidelines', 'nakigkita', 'siya', 'sa', 'nagkalainlaing', 'kompaniya', 'lakip', 'sa', 'Sanofi', ',', 'naghimo', 'sa', 'anti-dengue', 'vaccine', 'Dengvaxia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0] | cebuaner |
5,887 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Konsehal', 'Kevin', 'Sanchez', 'sa', 'Busay', 'niingon', 'nga', 'drayber', 'sa', 'Bonggo', 'Elf', 'ang', 'nagrabihan', 'pagmaayo', 'tungod', 'naipit', 'kini', 'sa', 'manobela', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,888 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kauban', 'niini', 'ang', 'asawa', 'sa', 'drayber', 'sa', 'Bonggo', 'ug', 'ang', 'driver', 'sa', 'Nissan', 'van', 'ug', 'gidala', 'dayon', 'kini', 'sa', 'ospital', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,889 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'atol', 'ning', 'pagsuwat', ',', 'wala', 'pa', 'makuha', 'ang', 'mga', 'pangalan', 'sa', 'biktima', 'human', 'gidali', 'pagdala', 'sa', 'ospital', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,890 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'ni', 'Sanchez', 'ang', 'katawhan', 'nga', 'mosuroy', 'sa', 'Busay', 'nga', 'maghinayhinay', 'aron', 'sa', 'paglikay', 'sa', 'mga', 'disgrasya.', '(', 'Mary', 'Diane', 'Salasayo', ',', 'USJR', 'intern', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0] | cebuaner |
5,891 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'suspek', 'gi-entrap', 'niadtong', 'Martes', 'ug', 'nasikop', 'sud', 'sa', 'usa', 'ka', 'lawak', 'sa', 'usa', 'ka', 'motel', 'kauban', 'ang', 'usa', 'sa', 'mga', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,892 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Princess', 'uban', 'sa', 'usa', 'ka', 'MJ', 'Joran', ',', '18', ',', 'taga', 'Talamban', ',', 'dakbayan', 'sa', 'Sugbo', 'maoy', 'kontak', 'ni', 'Cue', 'sa', 'pagpangita', 'og', 'mga', 'babaye', 'nga', 'dad-on', 'niini', 'sa', 'kama', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 2, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,893 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingon', 'unang', 'nakombinsir', 'ni', 'Princess', 'si', 'Anna', '(', 'di', 'tinuod', 'nga', 'pangan', ')', 'nga', 'makighimamat', 'ni', 'Cue', 'sanglit', '“meet-up”', 'ra', 'ang', 'gipasalig', 'niini', 'sa', 'suspek', 'ug', 'siya', 'bayran', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,894 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Villaro', 'nga', 'gibayaran', 'ang', 'biktima', 'og', 'P3,000', 'ni', 'Cue', ',', 'ang', 'P1,000', 'sa', 'maong', 'bayad', 'komisyon', 'sa', 'mga', 'recruiter', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,895 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'siyam', 'ka', 'adlaw', ',', 'ang', 'suod', 'nga', 'higala', 'ni', 'Anna', 'nga', 'si', 'Nicky', '(', 'di', 'tunod', 'pangan', ')', 'mao', 'na', 'usab', 'ang', 'nabiktima', 'nila', 'ni', 'Princess', 'ug', 'Jordan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0] | cebuaner |
5,896 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nisugot', 'matod', 'pa', 'nga', 'makighimamat', 'ang', 'biktima', 'ni', 'Cue', 'bisan', 'kini', 'giregla', 'niadtong', 'panahuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0] | cebuaner |
5,897 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Susamang', 'paagi', 'ang', 'gibuhat', 'sa', 'mga', 'recruiter', 'ang', 'gibuhat', 'niini', 'ni', 'Nicky', 'nga', 'maghuwat', 'lang', 'sila', 'sa', 'maong', 'mall', 'ug', 'kini', 'pick-upon', 'lang', 'sa', 'maong', 'langaw', ',', 'dad-on', 'sa', 'motel', 'ug', 'bayaran', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,898 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Disyembre', '8', ',', 'sila', 'si', 'Anna', 'ug', 'Nicky', 'nagkahibaw-anay', 'nga', 'gitunto', 'sila', 'sa', 'ilang', 'schoolmate', 'nga', 'recruiter', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,899 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Villaro', 'nga', 'kini', 'maoy', 'nakaaghat', 'sa', 'duha', 'sa', 'pagtug-an', 'sa', 'ilang', 'adviser', ',', 'kinsa', 'nisulti', 'sa', 'guidance', 'counselor', 'sa', 'ilang', 'tunghaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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