Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,200 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'matod', 'ni', 'CBST', 'Operations', 'Manager', 'Joey', 'Herrera', ',', 'anaa', 'sa', '700', 'ang', 'average', 'bus', 'trips', 'nga', 'ilang', 'matala', 'panahon', 'sa', 'tingsakay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,201 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pag-menus', 'sa', 'bus', 'trip', 'atol', 'sa', 'tingsakay', 'pagsugat', 'sa', 'Pasko', 'gitumbok', 'ni', 'Herrera', 'nga', 'tungod', 'sa', 'kalangay', 'pagbalik', 'sa', 'mga', 'bus', 'gikan', 'sa', 'ilang', 'biyahe', 'sa', 'habagatang', '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, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,202 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigun', 'sa', 'CSBT', 'management', ',', 'ang', 'grabeng', 'traffic', 'maoy', 'hinungdan', 'nganong', 'dugay', 'makabalik', 'ang', 'mga', 'bus', 'ug', 'maoy', 'nakaingon', 'nganong', 'daghan', 'ang', 'mangatanggong', 'nga', 'pasahero', '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, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,203 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Sabado', ',', 'ang', 'security', 'manager', 'sa', 'terminal', 'nga', 'si', 'Joy', 'Tumulak', ',', 'personal', 'nga', 'nihangyo', 'sa', 'Police', 'Regional', 'Office', '7', 'nga', 'mag-deploy', 'og', 'mga', 'pulis', 'nga', 'makatabang', 'pagbantay', 'sa', 'trapiko', 'sa', 'mga', 'lungsod', 'sa', 'southern', 'Cebu', ',', 'butang', 'nga', 'gipatalinghugan', 'dayon', 'ni', 'Chief', 'Supt.', 'Mario', 'Espino', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 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, 1, 2, 0] | cebuaner |
5,204 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'lungsod', 'sa', 'Minglanilla', 'maoy', 'gitumbok', 'sa', 'terminal', 'ug', 'bisan', 'gani', 'sa', 'mga', 'pasahero', ',', 'diin', 'nasinati', 'ang', 'grabeng', 'kahuot', 'sa', 'trapiko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,205 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingong', 'way', 'nakita', 'nga', 'mga', 'traffic', 'enforcer', 'sa', 'maong', 'lungsod', 'niadtong', 'Sabado', 'tungod', 'kay', 'nagpahigayon', 'sila', 'sa', 'ilang', 'Christmas', 'party', ',', 'nga', 'gihimakak', 'usab', 'sa', 'opisyal', 'sa', 'mao', 'nga', 'lungsod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,206 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'mga', 'netizen', 'ang', 'nakasaway', 'sa', 'mga', 'traffic', 'enforcer', 'sa', 'Minglanilla', 'kay', 'wa', 'nila', 'makit-i', 'dihang', 'naghuot', 'ug', 'wa', 'na', 'hapit', 'makairog', 'ang', 'taas', 'nga', 'trapiko', 'padung', 'sa', 'habagatan', 'ug', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,207 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Patay', 'ang', 'usa', 'ka', 'security', 'guard', 'nga', 'hubog', 'nagmaniho', 'sa', 'iyang', 'motorsiklo', 'ug', 'nakabangga', 'sa', 'gikasugat', 'nga', 'laing', 'motorsiklo', 'atol', 'sa', 'Pasko', 'sa', 'national', 'highway', 'sa', 'Barangay', 'Fuente', ',', 'lungsod', 'sa', 'Carmen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
5,208 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ian', 'Vincent', 'Perales', ',', '29', 'minyo', 'taga', 'Barangay', 'Poblacion', 'Carmen', ',', 'DOA', 'sa', 'tambalanan', 'samtang', 'grabe', 'ang', 'kahimtang', 'ni', 'Flaviano', 'Ornopia', ',', '31', ',', 'usa', 'ka', 'security', 'guard', 'taga', 'Barangay', 'Cogon', ',', 'lungsud', 'sa', 'Tabogon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
5,209 | 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', 'Christian', 'Torres', ',', 'nga', 'nahinabi', 'sa', 'Superbalita', 'nagkanayon', 'nga', 'mga', 'alas', '8:10', 'sa', 'gabii', 'Disyembre', '25', ',', 'giingong', 'hubog', 'ang', 'guwardiya', 'nga', 'si', 'Ornopia', 'nga', 'nagmaniho', 'sa', 'iyang', 'motorsiklo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
5,210 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'guwardiya', 'padulong', 'moduty', 'sa', 'Liloan', ',', 'gikasugat', 'niini', 'ang', 'gisakyan', '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, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,211 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'guwardiya', 'tungod', 'kay', 'hubog', 'kusog', 'nakapadagan', 'wala', 'na', 'mosubay', 'sa', 'lane', 'gidaro', 'ang', 'gisakyang', 'motorsiklong', 'gisakyan', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,212 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidali', 'sila', 'sa', 'pagdala', 'sa', 'tambalanan', 'pero', 'dead-on-arrival', 'ang', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,213 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'nabiktima', 'sa', 'sunog', 'sa', 'Barangay', 'Lawaan-3', ',', 'dakbayan', 'sa', 'Talisay', 'makadawat', 'og', 'financial', 'assistance', 'og', 'tag', 'P10', 'mil', 'alang', 'niadtong', 'maoy', 'tag-iya', 'sa', 'balay', 'nga', 'hingpit', 'nga', 'naugdaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,214 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kahinomduman', 'pipila', 'ka', 'oras', 'sa', 'wala', 'pa', 'ang', 'pagsaulog', 'sa', 'Pasko', 'usa', 'ka', 'dakong', 'sunog', 'ang', 'miulbo', 'sa', 'sityo', 'Caduldulan', ',', 'Barangay', 'Lawaan-3', ',', 'alas', '6:40', 'nadawat', 'ang', 'alarma', 'samtang', 'alas', '8:30', 'hingpit', 'nga', 'napawong', 'ang', 'kayo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,215 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'giingong', 'paggamit', 'og', 'butane', 'maoy', 'gitumbok', 'nga', 'hinungdan', 'sa', 'sunog', 'apan', 'ang', 'kabomebrohan', 'sa', 'Tlaisay', 'padayon', 'pa', 'sa', 'ilang', 'gihimong', 'inbestigasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,216 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibana-bana', 'nga', 'mokabat', 'sa', '400', 'mil', 'pesos', 'ang', 'gibilin', 'nga', 'danyos', 'sa', 'sunog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,217 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Felipa', 'Solana', 'ang', 'pangulo', 'sa', 'City', 'Social', 'Welfare', 'and', 'Services', 'miingon', 'nga', 'nga', 'base', 'sa', 'labing', 'uwahi', 'nilang', 'record', 'adunay', '96', 'ka', 'pamilya', 'kon', '384', 'ka', 'mga', 'individual', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,218 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', '80', 'ang', 'maoy', 'tag-iya', 'sa', 'mga', 'balay', '15', 'ang', 'nag-abang', 'ug', 'adunay', '87', 'ang', 'hingpitng', 'nga', 'nangaugdaw', 'ug', '9', 'ka', 'mga', 'balay', 'ang', 'naapektohan', 'lang', 'og', 'gamay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,219 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Solana', 'nga', 'ang', 'kagamhanan', 'sa', 'Talisay', 'mohatag', 'og', 'tag', 'P10', 'mil', 'alang', 'niadtong', 'mga', 'tag-iya', 'sa', 'balay', ',', 'samtang', 'kadtong', 'nag-abang', 'ug', 'hingpit', 'nga', 'naugdaw', 'ang', 'ilang', 'balay', 'tag', 'P5', 'mil', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,220 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iya', 'usab', 'nga', 'gibutyag', 'nga', 'ang', 'luna', 'nga', 'nasunog', 'gipanag-iya', 'sa', 'prebaong', 'tawo', 'adunay', 'pipila', 'niini', 'nga', 'nagbayad', 'sa', 'yuta', 'apan', 'uban', 'nga', 'wala', 'kadudahan', 'pa', 'kon', 'makabalik', 'ba', 'kining', 'tukod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,221 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'sa', 'pagkakaron', ',', 'ang', 'mga', 'nabiktima', 'sa', 'sunog', 'kasamtangan', 'karong', 'nagbutang', 'og', 'tent', 'sa', 'lugar', 'samtang', 'nahatagan', 'na', 'kining', 'pasiunang', 'tabang', 'sama', 'sa', 'mga', 'pagka-on', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,222 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mayor', 'Eduardo', 'Gullas', 'ug', 'Kongresista', 'Samsam', 'Gullas', 'personal', 'nga', 'nga', 'mibisita', 'sa', 'lugar', 'aron', 'mohatag', 'og', '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, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,223 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makubra', 'na', 'ang', 'libuan', 'ka', 'Cebu', 'City', 'scholars', 'sa', 'ilang', 'transportation', 'allowance', 'kinsa', 'nagpuyo', 'gikan', 'sa', 'mga', 'bukirang', 'barangay', 'ning', 'dakbayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,224 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mayor', 'Tomas', 'Osmeña', ',', 'kinsa', 'tua', 'sa', 'Estados', 'Unidos', ',', 'nianunsyo', 'nga', 'karong', 'Disyembre', '28', ',', 'ilang', 'makubra', 'ang', 'binuwan', 'nga', 'allowance', 'gikan', 'sa', 'city', 'government', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,225 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'city', 'scholars', 'kon', 'mga', 'tinun-an', 'nga', 'ni-graduate', 'sa', 'public', 'high', 'school', 'nga', 'karon', 'nag-eskuwela', 'na', 'sa', 'lainlaing', 'unibersidad', 'sa', 'dakbayan', 'sa', 'Sugbo', 'ug', 'nagpuyo', 'sa', 'mountain', 'barangay', 'adunay', 'allowance', 'gikan', 'sa', 'siyudad', 'gawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,226 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'city', 'scholar', 'giawhag', 'sa', 'pagpakigtambayayong', 'ngadto', 'sa', 'mga', 'Barangay', 'Mayor’s', 'Officer', 'sa', 'bukirang', 'barangay', 'sanglit', 'sagad', 'sa', 'ilang', 'mga', 'kapitan', 'dili', 'kaalyado', 'sa', 'administrasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,227 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'City', 'Treasurer', 'Veronica', 'Morelos', 'nagkanayon', 'nga', 'sa', 'niaging', 'semana', 'giatiman', 'na', 'nila', 'ang', 'payroll', 'ug', 'aduna', 'nay', 'kwarta', 'nga', 'gitagana', 'para', 'sa', 'pag-apud-apod', 'niini', 'ugmang', 'adlawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 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] | cebuaner |
5,228 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naukay', 'ang', 'mga', 'nag-shopping', 'sa', 'mall', 'human', 'nga', 'kalit', 'lang', 'nga', 'nilagobo', 'ang', 'ground', 'floor', 'ug', 'sa', 'ilang', 'pagsusi', ',', 'tawo', 'kini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,229 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dali', 'nga', 'gitabang', 'sa', 'security', 'personnel', 'ang', 'biktima', 'ug', 'gidala', 'sa', 'Chong', 'Hua', 'Hospital', 'sa', 'North', 'Reclamation', 'Area', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0] | cebuaner |
5,230 | 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', 'Mabolo', 'police', 'station', 'Chief', 'Insp.', 'Clark', 'Suarez', 'Arriola', 'nangunay', 'sa', 'pagsusi', 'sa', 'crime', 'scene', 'nitug-an', 'nga', 'ang', 'biktima', 'giila', 'nga', 'si', 'Rodrigo', 'Delos', 'Santos', 'Maglasang', ',', '45', ',', 'ulitawo', ',', 'engineer', ',', 'taga', 'Palompon', ',', 'Leyte', ',', 'apan', 'naa', 'nag', 'trabaho', 'sa', 'Commission', 'on', 'Elections', 'sa', 'Tuburan', 'isip', 'election', 'assistant', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 1, 2, 2, 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, 5, 6, 6, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 5, 0, 0, 0, 0] | cebuaner |
5,231 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidugang', 'ni', 'Arriola', 'nga', 'nagpadayon', 'pa', 'ang', 'ilang', 'imbestigasyon', 'kon', 'unsay', 'hinungdan', 'nga', 'miambak', 'si', 'Maglasang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0] | cebuaner |
5,232 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ipahigayon', 'karong', 'adlaw', 'nga', 'Huwebes', 'ang', 'public', 'bidding', 'sa', 'P150', 'milyones', 'para', 'sa', 'paghakot', 'sa', 'libuan', 'ka', 'toneladang', 'basura', 'sa', 'dakbayan', 'sa', 'Sugbo', 'sa', 'mosunod', 'nga', 'unom', 'ka', 'buwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,233 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kontrata', 'sa', 'Jomara', 'Konstruckt', 'Corporation', 'kinsa', 'maoy', 'service', 'provider', 'gikan', 'niadtong', 'buwan', 'sa', 'Agusto', 'gikatakdang', 'mapupos', 'karong', 'Disyembre', '31', 'diin', 'ang', 'City', 'Government', 'nagbayad', 'og', 'tag', 'P1,296', 'matag', '1,000', 'ka', 'kilong', 'basura', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 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 |
5,234 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'unya', 'sa', 'bidding', 'proper', ',', 'ang', 'adunay', 'labing', 'ubos', 'nga', 'bid', 'sa', 'tulo', 'ka', 'service', 'provider', 'maoy', 'madeklara', 'nga', 'mananaog', 'ug', 'hayag', 'na', 'ang', 'kahigayonan', 'nga', 'sila', 'ang', 'makakuha', 'sa', 'kontrata', 'sanglit', 'human', 'sa', 'bidding', 'isunod', 'dayon', 'sa', 'technical', 'working', 'group', 'ang', 'post', 'qualification', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,235 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'niaging', 'mga', 'kontrata', 'sa', 'paghakot', 'sa', 'basura', ',', 'ang', 'Jomara', 'ug', 'ang', 'Pamocor', 'nagpulipuli', 'og', 'daog', 'sa', 'bidding', 'sanglit', 'sila', 'ra', 'ang', 'klaro', 'nga', 'service', 'provider', 'nga', 'nipalit', 'og', 'bid', 'documents', 'apan', 'niining', 'bahin', 'tulo', 'na', 'sila', 'ang', 'magpaubsanay', 'sa', 'presyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 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 |
5,236 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aron', 'masiguro', 'sa', 'mga', 'piniriso', 'ingon', 'man', 'sa', 'publiko', 'nga', 'wa’y', 'recycled', 'nga', 'mahitabo', 'sa', 'mga', 'nakolekta', 'nila', 'nga', 'kontrabando', ',', 'gipakita', 'ni', 'Rubio', 'sa', 'mga', 'tigbalita', 'nga', 'ilahang', 'gisunog', 'ingon', 'man', 'giguba', 'ang', 'mga', 'nakuha', 'nila', 'nga', 'mga', 'kontrabando', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,237 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Rubio', 'nga', '80', 'porsyento', 'sa', 'maong', 'kontabando', 'gipanglabay', 'kini', 'gikan', 'sa', 'Operation', 'Second', 'chance', 'nga', 'pasilidad', 'diin', 'mga', 'bata', 'ang', 'gisugo', 'sa', 'paglabay', '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, 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 |
5,238 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'laing', '20', 'porsyento', 'mao', 'kini', 'ang', 'gisuwayan', 'pagpasulod', 'sa', 'mga', 'bisita', 'sa', 'prisohan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,239 | 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', 'kasagaran', 'sa', 'mga', 'kontrabando', 'mga', 'sigarilyo', 'diin', 'niabot', 'sa', 'kapin', '500', 'ka', 'mga', 'pakete', ',', '14', 'ka', 'mga', 'cell', 'phones', ',', 'chargers', '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, 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] | cebuaner |
5,240 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Subay', 'sa', 'ilang', 'hugot', 'nga', 'seguridad', 'sa', 'maong', 'prisohan', ',', '14', 'na', 'usab', 'ka', 'mga', 'bisita', 'ang', 'ilan', 'gi-ban', 'aron', 'di', 'na', 'ni', 'makapalusot', 'ingon', 'man', 'makasulod', 'sa', 'maong', 'pasilidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,241 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'ilang', 'nakuha', ',', 'way', 'bisan', 'usa', 'nga', 'drugas', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,242 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', 'ang', 'open', 'house', 'sa', 'CCJ', 'diin', 'gilauman', 'ang', 'pagdagsa', 'sa', 'tawo', 'alang', 'na', 'usab', 'sa', 'paghatag', 'og', 'kahigayonan', 'nga', 'makabisita', 'ang', 'ilang', 'pamilya', 'sa', 'mga', 'piniriso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,243 | 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', 'wa', 'siya’y', 'tawo', 'nga', 'gipa-break', 'o', 'gipabakasyon', 'aron', 'masiguro', 'nga', 'magpabilin', 'ang', 'seguridad', 'sa', 'maong', 'pasilidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,244 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'hingpit', 'na', 'nga', 'mahibalik', 'ang', 'biyahe', 'sa', 'bus', 'sa', 'Cebu', 'South', 'Bus', 'Terminal', ',', 'possible', 'nga', 'magnihit', 'kini', 'karong', 'adlawa', 'hilabi', 'na', 'nga', 'ang', 'mga', 'driver', 'moselebrar', 'usab', 'og', 'pasko', 'sa', 'tagsa-tagsa', 'nila', 'ka', 'mga', 'pamilya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,245 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'og', 'hingpit', 'nga', 'nahibalik', 'ang', 'mga', 'biyahe', 'paingon', 'sa', 'habagatang', '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, 5, 0] | cebuaner |
5,246 | 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', 'way', 'problema', 'ang', 'biyahe', 'gikan', 'sa', 'Sugbo', 'paingon', 'sa', 'Dumaguete', 'apan', 'ang', 'Dumaguete', 'paingon', 'sa', 'Dapitan', ',', 'wala', 'pa', 'higpit', 'nahibalik', 'human', 'ubos', 'pa', 'sa', 'Signal', 'No.', '1', 'ang', 'Zamboanga', 'atol', 'sa', 'pakighinabi', 'niini', 'sa', 'tigbalita', 'kagahapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,247 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', ',', 'aduna’y', 'mga', 'pasahero', 'nga', 'nibiyahe', 'na', 'paingon', 'sa', 'Dumaguete', 'aron', 'didto', 'navlang', 'maghuwat', 'paingon', 'sa', 'Dapitan', ',', 'Zamboanga', 'del', 'Norte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0] | cebuaner |
5,248 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'nahibalik', 'na', 'nga', 'biyahe', 'ang', 'paingon', 'sa', 'Bacolod', 'ug', 'Guihulngan', ',', 'Negros', 'Oriental', 'diin', 'manukad', 'kini', 'sa', 'Tangil', 'Wharf', 'sa', 'lungsod', 'sa', 'Dumanjug', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 6, 6, 0, 0, 0, 0, 3, 4, 0, 0, 0, 5, 0] | cebuaner |
5,249 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'nidagsa', 'na', 'ang', 'mga', 'pasahero', 'ug', 'nipahimangno', 'si', 'Tumulak', 'sa', 'mga', 'nagplano', 'pa', 'nga', 'mobiyahe', 'karong', 'adlawa', 'nga', 'dili', 'magdala', 'og', 'daghang', 'bagahe', 'nga', 'di', 'kinahanglanon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,250 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hilabina', 'sa', 'mga', 'butang', 'nga', 'di', 'pwede', 'isulod', 'sa', 'terminal', 'ug', 'sa', 'bus', ',', 'naglakip', 'sa', 'pabuto', ',', 'kemikal', ',', 'hinagiban', ',', 'pintal', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,251 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'puti', 'nga', 'van', 'nga', 'duha', 'pa', 'lang', 'ka', 'buwan', 'sukad', 'napalit', ',', 'gipanag-iya', 'sa', 'usa', 'ka', 'Rex', 'Salgariño', 'base', 'sa', 'resibo', 'nga', 'gipakita', 'ni', 'Rosito', 'Paquiao', ',', 'ang', 'drayber', 'sa', 'van', ',', 'nga', 'giisyuhan', 'og', 'Inspection', 'Report', 'Summo', '(', 'IRS', ')', 'sa', 'mga', 'tinugyanan', 'sa', 'LTFRB', '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, 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, 3, 4, 0] | cebuaner |
5,252 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Cuizon', ',', 'moabot', 'sa', 'P200,000', 'ang', 'multa', 'sa', 'masakpan', 'nga', 'colorum', 'van', 'ug', 'kini', 'maadto', 'sa', 'ilang', 'buhatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,253 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisumbalik', 'og', 'reklamo', 'sa', 'singer-negosyante', 'ug', 'sa', 'iyang', 'bana', 'ang', 'ilang', 'kanhi', 'employer', 'nga', 'nipasangil', 'nila', 'nga', 'nangilad', 'sa', 'gipalit', 'nga', 'P2.5', 'milyones', 'nga', 'townhouse', 'niadtong', '2016', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,254 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Eva', 'Delos', 'Santos', 'ug', 'ang', 'iyang', 'bana', 'nga', 'si', 'Ruben', 'David', ',', 'nipasaka', 'og', 'reklamong', 'libel', 'sa', 'Cebu', 'City', 'Prosecutors', 'Office', 'nga', 'nag-akusar', 'ni', 'Prisca', 'Niña', 'Mabatid', ',', 'tag-iya', 'sa', 'Pinoy', 'Care', 'Visa', 'Center', ',', 'nga', 'nidaot', 'sa', 'ilang', 'reputasyon', 'pinaagi', 'sa', 'live', 'interview', 'sa', 'Facebook', 'ug', 'sa', 'napublikar', 'sa', 'mga', 'pamantalaan', 'niadtong', 'Disyembre', '21', ',', '2017', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 1, 2, 2, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,255 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'magtiayon', 'nitumbok', 'nga', 'nasayod', 'nang', 'daan', 'si', 'Mabatid', 'nga', 'naka-loan', 'sa', 'Metrobank', 'ang', 'townhouse', 'sa', 'wala', 'pa', 'kini', 'gibaligya', 'ngadto', 'niya', 'ug', 'niuyon', 'kini', 'nga', 'maoy', 'mopadayon', 'sa', 'nahabilin', 'nga', 'balanse', 'sa', 'mortgage', 'nga', 'P1.9', '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, 0, 0, 0, 0, 0, 0, 0, 1, 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 |
5,256 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sila', 'nidugang', 'nga', 'bakak', 'usab', 'ang', 'pasangil', 'ni', 'Mabatid', 'nga', 'gibaligya', 'ngadto', 'sa', 'laing', 'tawo', 'ang', 'townhouse', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0] | cebuaner |
5,257 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Eva', 'nihilak', 'samtang', 'nagbasa', 'sa', 'ilang', 'pamahayag', 'sa', 'gipatawag', 'nga', 'press', 'conference', 'kagahapon', 'kuyog', 'ang', 'ilang', 'tulo', 'ka', 'mga', 'anak', 'nga', 'sila', 'si', 'Monique', ',', 'Ana', 'ug', 'James', 'Carlos', 'ug', 'sa', 'ilang', 'duha', 'ka', 'mga', 'lawyer', 'nga', 'sila', 'si', 'Atty.', 'Daniel', 'Francis', 'Arguedo', 'ug', 'Atty.', 'Patrick', 'Gallito', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 1, 2, 0] | cebuaner |
5,258 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nipasangil', 'nga', 'napalit', 'niya', 'ang', 'townhouse', 'gikan', 'sa', 'magtiayong', 'Delos', 'Santos', 'ug', 'nibayad', 'kini', 'og', 'P2.5', 'milyones', 'niadtong', 'Hunyo', '7', ',', '2016', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 0, 0, 0] | cebuaner |
5,259 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Mabatid', 'nga', 'gipalit', 'niya', 'ang', 'townhouse', 'gikan', 'kang', 'Eva', 'aron', 'tabangan', 'kini', 'tungod', 'kay', 'giingong', 'naglisod', 'kini', 'sa', 'panalapi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
5,260 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'magtiayon', 'nisumbalik', 'nga', 'wala', 'na', 'magbayad', 'sa', 'iyang', 'binuwan', 'nga', 'amortization', 'sa', 'banko', 'si', 'Mabatid', 'sukad', 'niadtong', 'Hulyo', 'ning', 'tuiga', 'human', 'kini', 'nila', 'gireklamo', 'og', 'illegal', 'dismissal', 'sa', 'National', 'Labor', 'Relations', 'Commission', '(', 'NLRC', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0] | cebuaner |
5,261 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'David', 'kanhi', 'nagtrabaho', 'sa', 'Pinoy', 'Care', 'sud', 'sa', 'usa', 'ka', 'tuig', 'ug', 'pito', 'ka', 'buwan', 'isip', 'speaker', 'ug', 'naghupot', 'sa', 'operations', 'sa', 'Middle', 'East', 'samtang', 'si', 'Eva', 'speaker', 'usab', 'sud', 'sa', 'dul-an', 'usa', 'ka', '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, 1, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,262 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'niadtong', 'Hulyo', '4', ',', '2017', ',', 'gitaktak', 'sila', 'ni', 'Mabatid', 'sa', 'trabaho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
5,263 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sila', 'nagtuo', 'nga', 'nanimawos', 'si', 'Mabatid', 'nila', 'ug', 'ni-pressure', 'nga', 'bakwion', 'ang', 'labor', 'case', 'nga', 'gipasaka', 'batok', 'niya', 'ug', 'sa', 'kompaniya', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,264 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apil', 'sa', 'gikiha', 'og', 'libel', 'ang', 'mga', 'editor', 'ug', 'tigbalita', 'sa', 'mga', 'mantalaan', 'apan', 'suma', 'pa', 'ni', 'Arguedo', ',', 'nagplano', 'sila', 'nga', 'bakwion', 'ang', 'reklamo', 'batok', 'sa', 'mga', 'sakop', 'sa', 'media', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,265 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'semana', 'sa', 'wa', 'pa', 'mi', 'nang-martsa', ',', 'namroblema', 'na', 'mi', 'ni', 'Renz', ',', 'akong', 'bestfriend', ',', 'unsay', 'among', 'sul-ubon', 'sanglit', 'gusto', 'namo', 'nga', 'di', 'malimtan', 'ang', 'labing', 'una', 'namong', 'Pride', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,266 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghan', 'naming', 'mga', 'plano', ',', 'gikan', 'sa', 'mga', 'see-through', 'shorts', ',', 'tank', 'tops', ',', 'ug', 'uban', 'pang', 'mga', 'beyga', 'nga', 'outfit', 'nga', 'nakit-an', 'namo', 'online', 'apan', 'way', 'bisan', 'usa', 'ang', 'napatahi', 'tungod', 'sa', 'ka-busy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,267 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'gyud', 'mi', 'alamag', 'unsay', 'mahitabo', 'sa', 'usa', 'ka', 'Pride', 'Parade', ',', 'ang', 'anaa', 'sa', 'among', 'mga', 'hunahuna', 'nga', 'daghang', 'mga', 'parehas', 'namo', 'nga', 'miyembro', 'sa', 'LBGT', '(', 'Lesbians', ',', 'Gays', ',', 'Bisexuals', 'and', 'Transgender', ')', 'community', 'ang', 'mangapil', 'sa', 'maong', 'kalihukan', 'uban', 'sa', 'mga', 'daghang', 'lami', 'nga', 'you', 'know', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,268 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'amoa’ng', 'hunahuna', ',', 'sunod', 'nalang', 'mis', 'panon', 'kay', 'di', 'man', 'mi', 'kahibaw', 'mo-Ininsik', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,269 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mas', 'engrande', 'ang', 'selebrasyon', 'ning', 'tuiga', 'tungod', 'kay', 'gawas', 'nga', 'ika-15', 'ka', 'tuig', 'na', 'nga', 'gihimo', 'sa', 'Taipei', 'ang', 'parada', ',', 'niadtong', 'Mayo', '24', ',', 'ang', 'Council', 'of', 'Grand', 'Justices', 'sa', 'maong', 'nasud', 'mi-anunsyo', 'sa', 'ilang', 'interpretasyon', 'sa', 'Civil', 'Code', 'sa', 'ilang', 'konstitusyon', 'nga', 'di', 'maki-angayon', 'nga', 'hikawan', 'sa', 'katungod', 'sa', 'pagpakasal', 'ang', 'mga', 'same-sex', 'couples', ',', 'pasabot', 'nga', 'uyon', 'sila', 'sa', 'same-sex', 'marriage', 'ug', 'niawhag', 'kini', 'sa', 'mga', 'magbabalaod', 'nga', 'usbon', 'ang', 'ilang', 'Civil', 'Code', 'sud', 'sa', 'duha', 'ka', 'tuig', 'ron', 'mapatuman', 'ang', 'maong', 'tinguha', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,270 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'sa', 'Taipei', 'Times', 'nga', 'record-breaking', 'ang', '123,000', 'ka', 'partisipante', 'ningsalmot', 'sa', 'parade', 'niadtong', 'Oktubre', '28', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,271 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'di', 'pa', 'nako', 'isaysay', 'akong', 'kasinatian', ',', 'gusto', 'kong', 'inyong', 'masabtan', 'nga', 'kining', 'artikulo', ',', 'di', 'ni', 'binastos', 'sa', 'kon', 'unsay', 'inyong', 'mga', 'tinuhu-an', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,272 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makapangutana', 'siguro', 'mo', 'kon', 'unsay', 'sinugdanan', 'nganong', 'halos', 'sa', 'tanang', 'mga', 'dagkong', 'dakbayan', 'ug', 'mga', 'dapit', 'sa', 'kalibutan', ',', 'magsaulog', 'ug', 'Pride', 'Parade', 'matag', '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, 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 |
5,273 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Maka-ana', 'siguro', 'mo', 'nga', 'wala', 'ra', 'moy', 'problema', 'sa', 'presensya', 'sa', 'mga', 'mahuyang', 'ninyo', 'nga', 'mga', 'igsuon', ',', 'higala', ',', 'classmate', ',', 'paryente', ',', 'kaila', 'ug', 'uban', 'pa', 'apan', 'ang', 'wa', 'ninyo', 'mahibaw-i', 'nga', 'sa', 'matag', 'minuto', 'sa', 'pagtuyok', 'ning', 'kalibutana', ',', 'adunay', 'usa', 'ka', 'miyembro', 'sa', 'LGBT', 'nga', 'giabuso', ',', 'gipasakitan', 'ug', 'gitamaktamakan', 'ang', 'tawhanong', 'katungod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,274 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'gabii', 'sa', 'ikaduhang', 'higayon', 'nga', 'pagbisita', 'nako', 'sa', 'nasud', 'niadtong', 'Disyembre', '2016', ',', 'nahimamat', 'ko', 'og', 'grupo', 'sa', 'mga', 'batan-on', 'nga', 'akong', 'abi', 'kog', 'namaligya', 'ug', 'pagkaon', 'gawas', 'sa', 'night', 'market', 'sa', 'Songshan', 'District', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 6, 0] | cebuaner |
5,275 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naggunit', 'sila', 'og', 'mga', 'placards', 'nga', 'inisik', ',', 'naninggit', 'ug', 'Ininsik', '(', 'murag', 'namaligya', 'ug', 'sud-an', ')', 'ug', 'usa', 'nila', 'niwara-wara', 'sa', 'rainbow', 'flag', ',', 'simbolo', 'sa', 'LGBT', 'community', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,276 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuryuso', 'ko', 'unsay', 'ilang', 'gibuhat', 'maong', 'nakig-istoryahanay', 'ko', 'nila', 'kung', 'unsay', 'ilang', 'giprotestaan', 'ug', 'kini', 'nagkanayon', 'nga', 'nipadayag', 'lang', 'sila', 'ug', 'pagsuporta', 'sa', 'same-sex', 'marriage', 'sa', 'ilang', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,277 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'minutos', 'sa', 'pakigplastikan', 'ug', 'chika', ',', 'nangutana', 'kos', 'akong', 'katabi', 'kon', 'usa', 'siya', 'ka', 'mahuyang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,278 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Didto', 'ko', 'nibilib', 'sa', 'akong', 'gika-istorya', 'nga', 'bisan', 'di', 'siya', 'bayot', 'apan', 'nisuporta', 'siya', 'tungod', 'ug', 'alang', 'sa', 'iyang', 'mga', 'higala', 'nga', 'miyembro', 'sa', 'LGBT', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 0] | cebuaner |
5,279 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niadto', ',', 'nakadisider', 'ko', 'nga', 'mosalmot', 'sa', 'Pride', 'ning', 'tuiga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,280 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'mga', 'posibleng', 'mahitabo', 'ug', 'masinati', 'nimu', 'sa', 'parade', ':'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,281 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nangasaag', 'ming', 'Renz', 'ug', 'akong', 'kauban', 'sa', 'buhat', 'nga', 'si', 'Chona', 'paingon', 'sa', 'venue.', 'Wa', 'sab', 'mi', 'nagkakita', 'sa', 'akong', 'Taiwanese', 'nga', 'higala', 'kay', 'nalangay', 'man', 'ang', 'mga', 'ebay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,282 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Imagina', ',', 'wa', 'mi', 'kahibaw', 'sa', 'rota', 'kon', 'asa', 'magsugod', 'ug', 'asa', 'mahuman', 'pero', 'werpa', 'gihapon', 'kon', 'asa', 'ang', 'mga', 'tawo', ',', 'sunod', 'lang', 'sa', 'panon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,283 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Didto', 'mi', 'nakakita', 'og', 'mga', 'lalake', 'nga', 'nag-cosplay', 'ug', 'Smurf', 'nga', 'dakog', 'bunal', ',', 'lalake', 'nga', 'nag-costume', 'og', 'anghel', 'ang', 'pako', ',', 'korona', 'ug', 'iyang', 'pribadong', 'parte', 'igo', 'lang', 'gitabunan', 'ug', 'gamayng', 'panapton', ',', 'mga', 'naggilak', 'nga', 'mga', 'gown', ',', 'make', 'up', ',', 'rainbow', 'flag', 'bisa’g-asa', 'ug', 'uban', 'pa.', 'Wa', 'kalabaw', 'among', 'gisul-ob', 'nga', 'jockstrap', 'ni', 'Renz', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,284 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'minutos', 'sa', 'among', 'pagsige’g', 'lakaw', ',', 'gipanggutom', 'mi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,285 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nalipong', 'sab', 'si', 'Chona', ',', 'maayo', 'nalang', 'nakakita', 'mi’g', 'McDonalds', 'duol', 'sa', 'Peace', 'Memorial', 'Park', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 0, 0, 5, 6, 6, 0] | cebuaner |
5,286 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naka-order', 'nami', 'ug', 'pagkaon', 'apan', 'wa', 'mi', 'lamesa', 'tungod', 'kay', 'puno', 'kinis', 'mga', 'costumer', 'maong', 'buwag-buwag', 'ming', 'tulo', 'ug', 'pangaon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,287 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'gihatagan', 'niya', 'og', 'importansiya', 'mao', 'ang', 'kabatan-onan', 'nga', 'malayo', 'sa', 'sakit', 'ug', 'magbiuotan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,288 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'nga', 'wala', 'siyay', 'mahatag', 'nga', 'gasa', 'alang', 'sa', 'tanang', 'Talisaynon', 'gawas', 'sa', 'pag-ampo', 'nga', 'hatagan', 'og', 'kabaskog', 'sa', 'panglawas', 'ang', 'taga', 'Talisay', 'ug', 'malayo', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0] | cebuaner |
5,289 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Midugang', 'siya', 'nga', 'unta', 'ang', 'kabataan', 'manggiluy-on', 'ug', 'dili', 'probelma', 'sa', 'ilang', 'mga', 'ginikanan', 'ug', 'sa', 'katilingban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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 |
5,290 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Gullas', 'nga', 'kon', 'ang', 'kabataan', 'adunay', 'pagtahod', 'sa', 'ilang', 'mga', 'maguwang', ',', 'walay', 'krimen', 'nga', 'mahitabo', 'ug', 'dili', 'usab', 'kini', 'makahimo', 'og', 'salaod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,291 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Gullas', 'niingon', 'nga', 'kinahanglan', 'usab', 'ang', 'taga', 'Talisay', 'motuo', 'sa', 'pag-ampo', 'iyang', 'gihimo', 'nga', 'sanglitanan', 'ang', 'iyang', 'kaugalingon', 'sa', 'dihang', 'nahimo', 'siyang', 'gobernador', 'sa', 'Sugbo', 'niadtong', 'kapin', 'pa', 'sa', '40', 'anyos', 'ang', 'iyang', 'pangidaron', 'na-diagnosed', 'og', 'lung', 'cancer', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,292 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingnan', 'siya', 'sa', 'iyang', 'doktor', 'nga', 'aduna', 'lang', 'siyay', 'tulo', 'ka', 'buwan', 'nga', 'mabuhi', ',', 'apan', 'tungod', 'sa', 'hugot', 'nga', 'pagtuo', 'sa', 'pag-ampo', 'sa', 'iyang', 'pamilya', 'ug', 'mga', 'higala', 'buhi', 'pa', 'siya', 'hangtod', '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, 0, 0, 0, 0, 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 |
5,293 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gibutyag', 'nga', 'grabe', 'ang', 'iyang', 'pag-ampo', 'ngadto', 'sa', 'Birhen', 'Maria', 'patron', 'sa', 'Talisay', 'ug', 'Sta', 'Teresa', 'de', 'Avila', 'ug', 'sa', 'balaang', 'bata', 'nga', 'si', 'Senyor', 'Sto.', 'Niño', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 5, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0] | cebuaner |
5,294 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nidugang', 'siya', 'nga', 'mahimo', 'na', 'unta', 'siyang', 'mohunong', 'sa', 'pagserbisyo', 'sa', 'publiko', 'ug', 'magpahayay', 'sa', 'nabiling', 'panahon', 'sa', 'iyang', 'kinabuhi', ',', 'apan', 'ang', 'iyang', 'kaikag', 'ug', 'kasasig', 'nagpadayon', 'sa', 'pagpangulo', 'sa', 'katawhan', 'aron', 'mapadayon', 'ang', 'iyang', 'pagserbisyo', 'ug', 'debosyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,295 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kinatibuk-ang', '2,809', 'ka', 'mga', 'pasahero', 'ang', 'nalangay', 'ang', 'mga', 'biyahe', 'gumikan', 'sa', 'kanselasyon', 'sa', 'mga', 'biyahe', 'sa', 'kadagatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,296 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'ihap', 'gikan', 'sa', 'lainlaing', 'mga', 'pantalan', 'sa', 'Sugbo', 'sama', 'sa', 'Hagnaya', ',', 'San', 'Remigio', ';', 'Polambato', ',', 'Bogo', 'City', ';', 'Tabuelan', ';', 'Bantayan', ';', 'Danao', ';', 'Consuelo', ',', 'San', 'Francisco', ',', 'Camotes', ';', 'siyudad', 'sa', 'Sugbo', '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, 5, 0, 0, 5, 6, 6, 6, 0, 5, 6, 6, 6, 0, 5, 0, 5, 0, 5, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 5, 0, 0, 0, 0] | cebuaner |
5,297 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pasado', 'sa', 'alas', '8', 'kagahapon', 'sa', 'buntag', ',', 'nibiyahe', 'ang', 'Oceanjet', 'gikan', 'sa', 'Cebu', 'padulong', 'sa', 'Ormoc', 'City', 'apan', 'gisugat', 'sa', 'dagkong', 'mga', 'bawod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,298 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niini', ',', 'nibalik', 'kini', 'sa', '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, 5, 0] | cebuaner |
5,299 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Lowangco', 'nga', 'siya', 'ug', 'kaubanan', 'nangahadlok', 'og', 'maayo', 'sa', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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