Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,500 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Catan', ',', 'ang', 'Sugbo', 'maoy', 'usa', 'sa', 'mga', 'lalawigan', 'nga', 'dunay', 'labing', 'taas', 'nga', 'maternal', 'death', 'rate', 'nga', 'moabot', 'sa', '20', 'sa', 'matag', '100,000', 'ka', 'mga', 'inahan.', 'Kini', 'tungod', 'usab', 'kay', 'wala', 'igong', 'pasilidad', 'ang', 'mga', 'district', 'ug', 'provincial', 'hospitals', 'pag-atiman', 'sa', 'ilang', 'mga', 'panginahanglan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,501 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'human', 'namatikdi', 'nga', 'nagkadaghan', 'karon', 'ang', 'mga', 'bata', 'ang', 'manaygun', 'sa', 'kagabion', 'diha', 'sa', 'SRP', 'habig', 'sa', 'dakbayan', 'sa', 'Talisay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 0] | cebuaner |
6,502 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'mga', 'motorsita', 'ang', 'mipadayag', 'sa', 'ilang', 'kabalaka', 'nga', 'kuyaw', 'ang', 'mga', 'bata', 'nga', 'madasmagan', 'tungod', 'kay', 'maglabang-labang', 'kini', 'sa', 'karsada', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,503 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'lakang', 'sa', 'pagpakuhag', 'permit', 'gihangop', 'usab', 'sa', 'kapulisan', 'sa', 'Talisay', 'aron', 'masiguro', 'nga', 'dili', 'mga', 'dautang', 'elemento', 'ang', 'nagsuroy-suroy', 'ug', 'nagpasumangil', 'lang', 'nga', 'manaygon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,504 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'PO2', 'John', 'Xyrus', 'Ilustrisimo', 'sa', 'Talisay', 'City', 'Police', 'nagkanayon', 'nga', 'seguridad', 'ug', 'pagpalig-on', 'sa', 'ilang', 'intelligence', 'monitoring', 'ang', 'ilang', 'gihimo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 2, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,505 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'bahin', 'si', 'Felipa', 'Solana', ',', 'pangu', 'sa', 'City', 'Social', 'Welfare', 'and', 'Development', '(', 'CSWD', ')', ',', 'nipahimangno', 'sa', 'mga', 'mga', 'ginikanan', 'nga', 'bantayan', 'ug', 'dili', 'pasagdan', 'ang', 'ilang', 'mga', 'anak', 'nga', 'magsuroy-suroy', 'aron', 'manaygon', 'kay', 'kon', 'masakpan', 'sila', 'maoy', 'posibling', 'mapahamtangan', 'og', 'silot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,506 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nanghinaot', 'si', 'Miral', 'nga', 'madala', 'niya’g', 'tarong', 'ang', 'mga', 'empleyado', 'labina', 'nga', 'ang', 'mga', 'simpatiya', 'sa', 'mga', 'tawo', 'naa', 'gihapon', 'ngadto', 'sa', 'napatay', 'nga', 'kapitan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,507 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pagsalig', 'sa', 'mga', 'katawhan', 'ngadto', 'kaniya', 'maoy', 'usa', 'sa', 'iyang', 'mga', 'tahas', 'nga', 'sagubangon', 'isip', 'bag-ong', 'lidera', 'aron', 'mas', 'mahiduol', 'ug', 'mas', 'manindot', 'ang', 'paglambo', 'sa', 'Barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,508 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mipasalig', 'ang', 'bag-ong', 'kapitan', 'nga', 'iyang', 'ipadayon', 'ang', 'proyekto', 'ni', 'anhing', 'Rupinta', 'nga', 'mao', 'ang', 'pagpatukod', 'og', 'eskuylahan', 'sa', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,509 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'eskuylahan', 'alang', 'sa', 'elementary', 'ug', 'high', 'school', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,510 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nangandoy', 'sab', 'si', 'Miral', 'nga', 'maminusan', 'ang', 'kawat', '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, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,511 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gimanduan', 'ang', 'mga', 'tanod', 'nga', 'perming', 'magpakita', 'sa', 'publiko', 'ilabi', 'na', 'sa', 'panahon', 'sa', 'gabii', 'diin', 'kusog', 'ang', 'kawat', 'ug', 'atol', 'sa', 'mga', 'okasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,512 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'sab', 'niya', 'nga', 'dili', 'lalim', 'ang', 'unom', 'ka', 'buwan', 'sa', 'paglingkod', 'ug', 'pagmugna', 'og', 'mga', 'tagas', 'nga', 'mga', 'plano', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,513 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Seminars', 'sa', 'mga', 'kabatanonan', ',', 'pagmugna', 'og', 'mga', 'aktibidades', 'sama', 'sa', 'sports', 'ug', 'pagpahiduol', 'sa', 'Ginoo', 'ug', 'pagsuporta', 'alang', 'sa', 'kaayohan', 'ang', 'mga', 'inisyal', 'pa', 'lang', 'nga', 'mga', 'plano', 'ni', 'Miral', 'sukad', 'sa', 'iyahang', 'paglingkod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] | cebuaner |
6,514 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagkakaron', 'ang', 'patayng', 'lawas', 'ni', 'Rupinta', 'anaa', 'sa', 'sports', 'complex', 'alang', 'sa', 'public', 'viewing', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,515 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikatakdang', 'ilubong', 'kini', 'karong', 'Biyernes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0] | cebuaner |
6,516 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'kang', 'Cesante', ',', 'laing', 'pito', 'pa', 'ka', 'mga', 'opisyal', 'sa', 'lungsod', 'ang', 'gipahamtangan', 'sa', 'susamang', 'suspensiyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,517 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigun', 'sa', 'Ombudsman', ',', 'nagpasagad', 'sa', 'ilang', 'trabaho', 'ang', 'mayor', 'ug', 'ang', 'mga', 'opisyal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,518 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'na', 'silang', 'gipasanginlan', 'nga', 'direktang', 'responsable', 'sa', 'pagkalunod', 'sa', 'usa', 'ka', 'fishing', 'boat', 'nga', 'iya', 'nilang', 'Russell', 'ug', 'Jowleyn', 'Heredia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 0] | cebuaner |
6,519 | 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', 'Ombudsman', ',', 'ang', 'panagatan', 'anaa', 'sa', 'kostudiya', 'sa', 'mga', 'respondent', 'human', 'nadakpan', 'subay', 'sa', 'kalapasan', 'sa', 'Fisheries', 'Code', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 7, 8, 0] | cebuaner |
6,520 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'dihang', 'niigo', 'ang', 'bagyong', 'Queenie', 'niadtong', 'Nobiyembre', '26', ',', '2014', ',', 'nalunod', 'ang', 'panagatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,521 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'na', 'unta', 'nga', 'nanghangyo', 'ang', 'mga', 'tag-iya', 'nga', 'ibalhin', 'sa', 'mas', 'luwas', 'nga', 'dapit', 'ang', 'bangka', 'nga', 'panagatan', ',', 'apan', 'wa', 'sila', 'pamaniwa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,522 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'Ombudsman', ',', 'gipasa-pasa', 'ang', 'mga', 'Heredia', 'aron', 'makalingkawas', 'sa', 'responsibilidad', 'sa', 'pagbalhin', 'sa', 'bangka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,523 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasubo', 'si', 'Cesante', 'nga', 'ang', 'ilang', 'pagtuman', 'sa', 'katungdanan', 'sa', 'pagpanalipod', 'sa', 'kalikopan', 'makapasuspenso', 'nila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,524 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nisaysay', 'nga', 'human', 'makasohi', 'og', 'kalapasan', 'sa', 'Fisheries', 'Law', ',', 'ang', 'mga', 'tag-iya', 'og', 'panagatan', 'nisumbalik', 'og', 'reklamo', 'nila', 'og', 'gross', 'neglect', 'of', 'duty', 'batok', 'niya', 'ug', 'ubang', 'mga', 'opisyal', 'sa', 'lungsod', 'human', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,525 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nanghinaot', 'nga', 'mahatagan', 'og', 'konsiderasyon', 'sa', 'Ombudsman', 'ang', 'ilang', 'giduso', 'nga', 'motion', 'for', 'reconsideration', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0] | cebuaner |
6,526 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikatakdang', 'mopalabang', 'sunod', 'semana', 'og', 'balaudnon', 'sa', 'Ubos', 'Balay', 'Balauranan', 'si', 'Kongresista', 'Raul', 'del', 'Mar', 'para', 'matugotan', 'nga', 'makapamasahero', 'ang', 'mga', 'motorsiklo', '(', 'two-wheeled', 'vehicle', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,527 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'del', 'Mar', 'kinsa', 'dinapit', 'nga', 'bisita', 'atol', 'sa', 'unity', 'ride', 'sa', 'kapin', '1,000', 'ka', 'mga', 'habalhabal', 'driver', 'kinsa', 'akreditado', 'sa', 'Angkas', 'niingon', 'nga', 'ang', 'maong', 'problema', 'maoy', 'iyang', 'nakita', 'human', 'usab', 'kini', 'gisuspenso', 'sa', 'Land', 'Transportation', 'Franchising', 'and', 'Regulatory', 'Board', '(', 'LTFRB', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0] | cebuaner |
6,528 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'gipadayag', 'ni', 'del', 'Mar', 'nga', 'di', 'madali', 'ang', 'pag-amendar', 'sa', 'provision', 'sa', 'RA', '4136', 'kinsa', 'nagdili', 'sa', 'mga', 'motorsiklo', 'nga', 'magamit', 'isip', 'public', 'transport', 'sanglit', 'molanat', 'kini', 'og', 'labing', 'dali', '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, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,529 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Dr.', 'Jaime', 'Bernadas', ',', 'regional', 'director', 'sa', 'DOH', '7', ',', 'nipasabot', 'ang', 'severe', 'dengue', 'kauban', 'sa', 'mild', 'ug', 'moderate', 'kay', 'daan', 'nang', 'klasipikasyon', 'sa', 'dekada', '1990', 'ug', 'wa', 'na', 'nila', 'gamita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,530 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'ilang', 'gigamit', 'karon', 'kay', 'stage', '1', ',', '2', ',', '3', 'ug', '4', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,531 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'sa', 'daan', 'nga', 'klasipikasyon', 'kon', 'moingon', 'og', '‘severe', 'dengue’', 'ang', 'nataptan', 'niini', ',', 'nagpakita', 'sa', 'mga', 'simtomas', 'sa', 'dengue', 'ug', 'nikunhod', 'ang', 'platelet', 'count', 'ubos', 'sa', '100,000', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,532 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bernadas', 'niingon', 'mahinungdanon', 'nga', 'mahibaw-an', 'nila', 'kon', 'unsay', 'pasabot', 'sa', 'Sanofi', 'aron', 'mahibaw-an', 'kon', 'unsa', 'nga', 'klase', 'sa', 'epekto', 'ang', 'masinati', 'sa', 'bata', 'nga', 'nabakunahan', 'ug', 'magiyahan', 'ang', 'tanan', 'unsay', 'angay', 'ipahibawo', 'ngadto', 'sa', 'mga', 'ginikanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 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 |
6,533 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'nasabotan', 'nga', 'kontrata', 'kay', 'mobalor', 'og', 'P22.6', 'bilyones', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,534 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Cebu', 'Cordova', 'Link', 'Expressway', 'Corp.', '(', 'CCLEC', ')', 'maoy', 'niisyu', 'sa', 'Notice', 'of', 'Award', 'sa', 'CLJV', 'niadtong', 'Nobiyembre', '23', ',', '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, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0] | cebuaner |
6,535 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'CLJV', 'usa', 'ka', 'joint-venture', 'sa', 'Acciona', 'Construccion', 'S.A.', ',', 'nga', 'nagbase', 'sa', 'Espanya', 'ug', 'First', 'Balfour', 'Inc.', 'nagbase', 'sa', 'Pilipinas', 'ug', 'D.M.', 'Consunji', 'Inc.', ',', 'nga', 'pulos', 'dunay', 'maayo', 'nga', 'track', 'records', 'sa', 'nag-unang', 'mga', 'proyekto', 'sa', 'imprastruktura', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 4, 0, 0, 0, 0, 5, 0, 1, 2, 2, 0, 0, 5, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,536 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'MPTC', 'ang', 'labing', 'kinadak-an', 'nga', 'toll', 'road', 'concessionaire', 'ug', 'operator', 'sa', 'Pilipinas', 'nga', 'nipalapad', 'sa', 'iyang', 'operasyon', 'sa', 'ubang', 'mga', 'nasod', 'sa', 'Asya', 'sama', 'sa', 'Thailand', ',', 'Vietnam', 'ug', 'Indonesia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0] | cebuaner |
6,537 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'CCLEC', 'president', 'ug', 'general', 'manager', 'Allan', 'Alfon', 'masaligon', 'nga', 'mahatag', 'nila', 'ang', 'dekalidad', 'nga', 'state-of-the-art', 'nga', 'taytayan', 'nga', 'di', 'lang', 'mopasayon', 'sa', 'biyahe', 'kondi', 'mopalambo', 'usab', 'sa', 'ekonomiya', 'sa', 'Kabisay-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, 3, 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, 7, 0] | cebuaner |
6,538 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Presidente', 'Rodrigo', 'Duterte', 'ang', 'nangu', 'sa', 'groundbreaking', 'ceremony', 'sa', 'proyekto', 'niadtong', 'Marso', '2', ',', '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, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,539 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'ka', 'langyaw', 'nga', 'mga', 'contractor', 'ang', 'ni-qualify', 'sa', 'bidding', 'ug', 'nilagda', 'og', 'joint-venture', 'agreement', 'sa', 'lokal', 'nga', 'mga', 'contractor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,540 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tinguha', 'nga', 'mahuman', 'ang', 'katukoran', 'sa', 'ikatulong', 'taytayan', 'sa', '2020', 'o', 'maatol', 'usab', 'nga', 'saulogon', 'ang', 'ika', '500', 'ka', 'tuig', 'nga', 'paghandum', 'sa', 'pagsugod', 'sa', 'Kristiyanismo', 'sa', 'Pilipinas', 'labi', 'na', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 5, 0, 0, 0, 5, 0] | cebuaner |
6,541 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hatagan', 'og', 'lima', 'ka', 'adlaw', 'sa', 'Land', 'Transportation', 'Office', 'ang', 'drayber', 'sa', 'taxi', 'sa', 'Gilgal', 'Transport', 'Cebu', 'Corp.', 'nga', 'gipasanginlan', 'nangayo', 'og', 'sobrang', 'plitehan', 'sa', 'iyang', 'langyaw', 'nga', 'mga', 'pasahero', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,542 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Joel', 'Maloloy-on', ',', 'operations', 'head', 'sa', 'LTO', '7', ',', 'nibutyag', 'nga', 'isip', 'kabahin', 'sa', 'due', 'process', ',', 'hatagan', 'nila', 'og', 'lima', 'ka', 'adlaw', 'si', 'Jack', 'Espanto', 'paghatag', 'sa', 'iyang', 'habig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0] | cebuaner |
6,543 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Espanto', 'gipasanginlan', 'nga', 'nangayo', 'og', 'P2,000', 'nga', 'pletihan', 'gikan', 'sa', 'usa', 'ka', 'hotel', 'sa', 'siyudad', 'sa', 'Lapu-Lapu', 'paingon', 'sa', 'SM', 'Seaside', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
6,544 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'giisyu', 'nga', 'TOP', 'sa', 'LTFRB', '7', ',', 'P18,000', 'ang', 'bayranan', 'nga', 'multa', 'ni', 'Espanto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,545 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '12,544', 'ka', 'bag-ong', 'botante', 'mahingpit', 'ang', 'ilang', 'pagsalmot', 'sa', 'bugnong', 'lugaynon', 'sa', 'higayon', 'nga', 'maaprobahan', 'na', 'sila', 'sa', 'Election', 'Registration', 'Board', '(', 'ERB', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 3, 4, 4, 4, 4, 4, 0] | cebuaner |
6,546 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sama', 'sa', 'naandan', ',', 'ang', 'katapusang', 'adlaw', 'sa', 'rehistrasyon', '(', 'November', '30', ')', 'ang', 'adunay', 'labing', 'daghan', 'nga', 'nagparehistro', 'sa', '24', 'ka', 'adlaw', 'nga', 'gihatag', 'sa', 'Comelec', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0] | cebuaner |
6,547 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Comelec', 'wala', 'nitugot', 'nga', 'adunay', 'botante', 'nga', 'makabalhin', 'sa', 'ilang', 'pagparehistro', 'sanglit', 'gitagana', 'ang', '24', 'ka', 'adlaw', 'sa', 'bag-ong', 'botante', 'nga', 'gustong', 'mobotar', 'sa', 'piniliay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,548 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'eleksyon', 'niadtong', 'tuig', '2016', ',', 'ang', 'Cebu', 'City', 'adunay', '630,003', 'ka', 'mga', 'botante', 'diin', '524,850', 'ka', 'mga', 'tawo', 'ang', 'niboto', 'kon', '83.30', 'porisyento', 'ang', 'nisalmot', 'sa', 'piniliay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,549 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sama', 'sa', 'unang', 'nasikop', 'nga', 'si', 'Jimmy', 'Largo', ',', 'nag-atubang', 'na', 'usab', 'siya', 'sa', 'mga', 'kasong', 'murder', 'ug', 'attempted', 'murder', 'subay', 'sa', 'kamatayon', 'ni', 'Rupinta', 'ug', 'sa', 'suway', 'nga', 'pagpatay', 'sa', 'kapuyo', 'niini', 'nga', 'si', 'Jocelyn', 'Mendoza', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,550 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mendoza', 'niila', 'na', 'usab', 'kang', 'Gera', 'isip', 'usa', 'sa', 'mga', 'nipaulan', 'ug', 'bala', 'sa', 'ilang', 'sakyanan', 'sa', 'Barangay', 'Yati', ',', 'Liloan', ',', 'atol', 'sa', 'ilang', 'panag-abot', 'sa', 'piskaliya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,551 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'abogado', 'si', 'Gera', 'atol', 'sa', 'inquest', 'apan', 'nidesisyon', 'siya', 'nga', 'dili', 'na', 'moduso', 'ug', 'counter', 'affidavit', 'ug', 'adto', 'na', 'sa', 'korte', 'motubag', 'sa', 'mga', 'pasangil', 'batok', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,552 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'piyansa', 'nga', 'girekomendar', 'alang', 'sa', 'temporaryong', 'kagawasan', 'ni', 'Gera', 'tungod', 'kay', 'usa', 'ka', 'non-bailable', 'offense', 'ang', 'murder', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,553 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'mga', 'gigamit', 'nga', 'ebidensiya', 'batok', 'ni', 'Gera', 'mao', 'ang', 'motorsiklo', 'nga', 'giingong', 'gigamit', 'niini', 'atol', 'sa', 'krimen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0] | cebuaner |
6,554 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Supt.', 'Reynaldo', 'Magdaluyo', 'sa', 'Regional', 'Special', 'Operations', 'Group', '(', 'RSOG', ')', '7', 'nangayo', 'lang', 'una', 'ug', 'pagsabot', 'nganong', 'dili', 'pa', 'nila', 'itug-an', 'kon', 'kinsa', 'ang', 'nagpaluyo', 'sa', 'pagpapatay', 'kang', 'Rupinta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,555 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', ',', 'nakaila', 'na', 'sila', 'niini', 'ug', 'nagpadayon', 'sab', 'ang', 'ilang', 'pag-gukod', 'niadtong', 'ubang', 'suspek', 'nga', 'posibleng', 'moabot', 'pa', 'ngadto', 'sa', '5', 'ka', 'mga', 'indibdiwal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,556 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dunay', 'mga', 'kalihokan', 'ang', 'Carbon', 'Public', 'Market', 'atol', 'Pasko', ',', 'diin', 'dunay', '12', 'nga', ''Carbon', 'Pasko', '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, 3, 4, 4, 0, 7, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0] | cebuaner |
6,557 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pagpahigayon', 'og', 'Misa', 'de', 'Gallo', 'maoy', 'kinadayagan', 'nga', 'kalihokan', 'sa', 'maong', 'dapit', 'nga', 'sugdan', 'sa', 'petsa', '16', 'ug', 'matapos', 'sa', 'petsa', '24', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,558 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Carbon', 'Pasko', 'Na', 'usa', 'ka', 'kalihokan', 'nga', 'magkasuod', 'angmanindahay', 'ug', 'mamalitay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,559 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nibahad', 'si', 'Mandaue', 'City', 'Mayor', 'Luigi', 'Quisumbing', 'niadtong', 'mga', 'buot', 'maninda', 'og', 'paboto', 'nga', 'mokuha', 'gyud', 'og', 'permit', 'sa', 'dakbayan', 'usa', 'mamaligya', 'aron', 'di', 'embarguhon', 'sa', 'kapulisan', 'ang', 'gipangbaligya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,560 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'pa', 'sa', 'mayor', 'nga', 'sa', 'mga', 'mall', ',', 'bugtong', 'mga', 'pyrotechnics', 'lang', 'ang', 'gitugutan', 'nga', 'mabaligya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,561 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidili', 'sab', 'ang', 'pagpamaligya', 'og', 'paboto', 'sa', 'mga', 'dalan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,562 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasuta', 'ning', 'mantalaan', 'gikan', 'ni', 'Quisumbing', 'nga', 'sagad', 'sa', 'mga', 'namaligya', 'og', 'paboto', 'niadto', 'way', 'mga', 'permiso', 'sa', 'city', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,563 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'usab', 'ni', 'Bise', 'Mayor', 'Carlo', 'Fortuna', 'nga', 'ila', 'unyang', 'tahasan', 'ang', 'kapulisan', 'ug', 'mga', 'tanod', 'sa', 'pag-inspeksyon', 'sa', 'merkado', 'aron', 'masikop', 'ang', 'mga', 'namaligya’g', 'paboto', 'nga', 'way', 'permiso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,564 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'pagbantay', 'sa', 'iligal', 'nga', 'pagpamaligya', 'og', 'paboto', ',', 'si', 'Quisumbing', 'niingon', 'nga', 'alerto', 'usab', 'ang', 'dakbayan', 'karong', 'kapaskuhan', 'batok', 'sa', 'dautang', 'mga', 'elemento', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,565 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Disyembre', '1', 'nga', 'maoy', 'labing', 'uwahing', 'batch', 'nga', 'gihatagan', 'og', 'pahalipay', 'sa', 'Lapu-Lapu', 'City', 'government', 'diin', 'moabot', 'sa', '52', 'ang', 'gihatagan', 'og', 'tag-P5,000', 'ang', 'matag', 'passers', 'sa', 'bisan', 'unsa', 'nga', 'kurso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,566 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'kadtong', 'nasulod', 'sa', 'Top', '10', 'mas', 'dako', 'nga', 'kandtidad', 'ang', 'ilang', 'madawat', 'diin', 'ang', 'first', 'placer', 'makadawat', 'og', 'P100,000', 'samtang', 'P90,000', 'alang', 'niadtong', 'second', 'placer', ',', 'P80,000', 'sa', 'third', 'placer', 'hangtod', 'nga', 'magka-ubos', 'ang', 'ranking', 'kutob', 'sa', 'Top', '10', 'nga', 'adunay', 'corresponding', 'cash', 'aid', 'nga', 'makuha', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,567 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Una', 'nang', 'namahayag', 'si', 'Mayor', 'Paz', 'Radaza', 'nga', 'angayan', 'lang', 'hatagan', 'og', 'pasidungog', 'ug', 'pahalipay', 'ang', 'mga', 'nipasar', 'sa', 'board', 'ug', 'licensure', 'examination', 'ang', 'maong', 'mga', 'Oponganon', 'sanglit', 'naghatag', 'sila', 'og', 'dakong', 'dungog', '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, 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,568 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagdasig', 'usab', 'kini', 'sa', 'ubang', 'tinun-an', 'nga', 'mokugi', 'ug', 'maningkamot', 'sa', 'ilang', 'pagtuon', ',', 'dugang', 'pa', 'ni', 'Radaza', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,569 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kinabuhi', 'sa', 'tawo', 'gikaluhaan', 'na', 'og', 'mga', 'hagit', 'ug', 'pagsulay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,570 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'bisan', 'pa', 'niini', ',', 'angay', 'nga', 'di', 'mawagtangan', 'og', 'paglaom', 'ang', 'tawo', 'kay', 'sama', 'sa', 'panultion', ',', 'adunay', 'bangaw', 'nga', 'magpakita', 'matag', 'human', 'sa', 'ulan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,571 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gani', ',', 'ang', 'mapait', 'nga', 'kagahapon', 'mao', 'ang', 'gihimong', 'inspirasyon', 'aron', 'iyang', 'sagubangon', 'ang', 'mga', 'pagsuway', 'sa', 'kinabuhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,572 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sama', 'kini', 'sa', 'sugilanon', 'sa', 'kinabuhi', 'ni', 'Robert', 'Lawrence', 'Yap', ',', '30', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0] | cebuaner |
6,573 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'dihang', 'batan-on', 'pa', 'kini', ',', 'wa', 'siya’y', 'ambisyon', 'sa', 'kinabuhi.', 'Ang', 'iyaha', 'lang', 'mamasahero', 'og', 'motor', 'ug', 'multicab', 'isip', 'panginabuhian', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,574 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nausab', 'ang', 'iyang', 'panglantaw', 'sa', 'kinabuhi', 'dihang', 'tulo', 'sa', 'mga', 'miyembro', 'sa', 'iyang', 'pamilya', 'ang', 'anam-anam', 'nga', 'gikuha', 'sa', 'kahitas-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] | cebuaner |
6,575 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Laing', 'pagsulay', 'ang', 'niabot', 'sa', 'ilang', 'pamilya', 'sa', 'dihang', 'nasakit', 'og', 'brain', 'tumor', 'ang', 'ilang', 'amahan', 'nga', 'si', 'Roger', 'Yap', 'ug', 'wala', 'na', 'makatrabaho', 'isip', 'masyador', 'sa', 'buwangan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,576 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naglisod', 'ang', 'ilang', 'pamilya', 'kay', 'kuwangon', 'ra', 'ang', 'sweldo', 'sa', 'ilang', 'inahan', 'nga', 'si', 'Rosalinda', 'nga', 'usa', 'ka', 'maestra', 'sa', 'publikong', 'tunghaan', ',', 'sa', 'pagpalit', 'og', 'mga', 'tambal', 'sa', 'ilang', 'amahan', 'ug', 'sa', 'ilang', 'inadlaw-adlaw', 'nga', 'panginahanglan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 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 |
6,577 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'ilang', 'amahan', 'namatay', 'tulo', 'ka', 'adlaw', 'sa', 'dili', 'pa', 'ang', 'birthday', 'niini', 'ug', 'gilubong', 'sa', 'mismo', 'adlaw', 'sa', 'kasumaran', 'sa', 'iyang', 'adlaw', 'nga', 'natawhan', 'niadtong', 'Disyembre', '31', ',', '2005', 'kun', 'besperas', 'sa', 'Bag-ong', '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, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
6,578 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naminyo', 'og', 'usab', 'si', 'Rosalinda', 'ug', 'tua', 'na', 'magbase', 'sa', 'Estados', 'Unidos', 'diin', 'si', 'Robert', 'ang', 'apil', 'sa', 'gipetisyonan', 'ug', 'nakatrabaho', 'didto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 6, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,579 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dihang', 'tua', 'na', 'siya', 'sa', 'Florida', ',', 'USA', ',', 'lainlain', 'nga', 'klase', 'sa', 'trabaho', 'ang', 'nasuwayan', 'ni', 'Robert', 'gikan', 'sa', 'pagka', 'caregiver', 'sud', 'sa', 'lima', 'ka', 'tuig', 'hangtod', 'sa', 'pagka', 'dishwasher', 'dayon', 'nahimo', 'nga', 'prep', 'cook', 'hangtod', 'wok', 'cook', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,580 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sud', 'sa', 'pito', 'ka', 'adlaw', 'sa', 'matag', 'semana', 'kon', 'siya', 'motrabaho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,581 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Robert', 'niasoy', 'nakasugat', 'siya', 'og', 'mga', 'kalisod', 'sa', 'iyang', 'pagpanarbaho', 'sa', 'laing', 'nasod', 'sama', 'sa', 'pakig-estorya', 'sa', 'pinulungan', 'nga', 'English', ',', 'kuwang', 'og', 'skills', ',', 'ug', 'siya', 'mauwawon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,582 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hapit', 'mawagtangan', 'og', 'paglaom', 'si', 'Robert', 'apan', ',', 'naningkamot', 'siya', 'nga', 'makatutok', 'sa', 'trabaho', 'alang', 'sa', 'iyang', 'inahan', 'ug', 'manghod', 'nga', 'babaye', 'nga', 'si', 'Mary', 'Grace', ',', '24', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0] | cebuaner |
6,583 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gumikan', 'sa', 'kakugihan', 'motrabaho', ',', 'si', 'Robert', 'nakatigom', 'og', 'P5', 'milyones', 'nga', 'maoy', 'gipundar', 'niini', 'og', 'negosyo', ',', 'usa', 'ka', 'kan-anan', 'sa', 'lungsod', 'sa', 'Argao', ',', 'habagatang', 'Sugbo', 'ug', 'gipangan', 'kini', 'sa', 'iyang', 'maguwang', 'nga', 'si', 'Eton', ',', '“Carenderia', 'ni', 'Eton.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 7, 8, 8] | cebuaner |
6,584 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nidugang', 'nga', 'bisan', 'unsa', 'pa', 'nga', 'hagit', 'ang', 'moabot', 'sa', 'kinabuhi', ',', 'padayon', 'nga', 'maningkamot', 'ug', 'dili', 'mawagtangan', 'sa', 'paglaom', 'ug', 'pagsalig', 'sa', 'kaugalingon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
6,585 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'gibutyag', 'sa', 'pangulo', 'sa', 'Cebu', 'PHO', 'Dr.', 'Rene', 'Catan', 'nimando', 'na', 'siya', 'sa', 'tanang', 'city', 'ug', 'muncipal', 'health', 'units', 'sa', 'pagsubay', 'ug', 'paghimo', 'sa', 'listahan', 'nga', 'nabakonahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,586 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'lakang', 'nga', 'gihimo', 'sa', 'PHO', 'human', 'sa', 'pahibalo', 'gikan', 'sa', 'Sanofi', 'S.A.', ',', 'tiggama', 'sa', 'Dengvaxia', 'vaccine', 'nga', 'dunay', 'dili', 'maayong', 'epekto', 'kini', 'sa', 'wala', 'pa', 'masakit', 'og', '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, 3, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,587 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'usab', 'Catan', 'ang', 'mga', 'ginikanan', 'sa', 'mga', 'bata', 'nga', 'nabakonahan', 'sa', 'pagduol', 'sa', 'ilang', 'duol', 'nga', 'city', 'ug', 'municipal', 'health', 'offices', 'aron', 'ma-monitor', 'ang', 'panglawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 0, 0, 0, 0, 0] | cebuaner |
6,588 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Catan', 'nisulti', 'sa', 'SunStar', 'Cebu', 'nga', 'ang', 'DOH', '7', 'nagsugod', 'sa', 'papatuman', 'sa', 'pag-apud-apod', 'sa', 'vaccines', 'sa', 'kalungsoran', 'ug', 'component', 'cities', 'ubos', 'sa', 'Cebu', 'province', ',', 'apan', 'wala', 'nila', 'giapil', 'ang', 'PHO', ',', 'ang', 'district', 'ug', 'provincial', 'hospitals', 'sa', 'monitoring', 'process', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,589 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Catan', ',', 'gitahasan', 'lang', 'sila', 'sa', 'pag-ila', 'sa', 'mga', 'bata', 'nga', 'ipaubos', 'sa', 'pagpakona', 'ug', 'wala', 'apil', 'ang', 'pag-monitor', 'sa', 'ilang', 'panglawa', 'human', 'sa', 'pagbakona', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,590 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giapasabot', 'ni', 'Catan', 'nga', 'bugtong', 'ang', 'Vicente', 'Sotto', 'Memorial', 'Medical', 'Center', '(', 'VSMMC', ')', 'ang', 'gitahasan', 'sa', 'pag-monitor', 'sa', 'status', 'sa', 'nabakonahan', 'nga', 'bata', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,591 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'VSMMC', ',', 'usa', 'ka', 'tertiary-level', 'hospital', ',', 'ubos', 'sa', 'direkta', 'nga', 'pagdumala', 'sa', 'DOH', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 0] | cebuaner |
6,592 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sayo', 'ning', 'tuiga', ',', 'si', 'Catan', 'niluwat', 'og', 'oposisyon', 'sa', 'plano', 'sa', 'DOH', 'sa', 'pag-apud-apod', 'sa', 'dengue', 'vaccine', 'sa', 'Cebu', 'province', 'human', 'ang', 'tighimo', 'niini', 'walay', 'klaro', 'sa', 'ilang', 'epekto', 'sa', 'bakona', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,593 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'DOH', 'nipadayon', 'sa', 'plano', 'sa', 'pag-apud-apod', 'sa', 'bakona', 'niadtong', 'Hunyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,594 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'DOH', 'mopadayon', 'unta', 'sa', 'ikaduhang', 'hugna', 'sa', 'pagpakona', 'subay', 'sa', 'programa', 'sa', 'sunod', 'tuig', 'apan', 'nasuta', 'kini', 'nga', 'nakamugna', 'og', 'grabe', 'o', 'severe', 'dengue', 'fever', 'sa', 'mga', 'bata', 'nga', 'wala', 'pa', 'matapti', 'sa', 'virus', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0] | cebuaner |
6,595 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakita', 'usab', 'sa', 'Sanofi', 'S.', 'A.', 'nga', 'dunay', 'mas', 'grabe', 'pa', 'nga', 'kaso', 'dengue', 'virus', 'human', 'nabakonahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,596 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'ulahing', 'nasuta', 'sa', 'manufacturer', ',', 'si', 'DOH', 'Secretary', 'Francisco', 'Duque', 'nipahibalo', 'sa', 'miaging', 'semana', 'sa', 'pagsuspenso', 'sa', 'programa', 'hangtod', 'ang', 'Sanofi', 'S.A.', 'ug', 'ang', 'World', 'Health', 'Organization', '(', 'WHO', ')', 'mohatag', 'og', 'tambag', 'sa', 'pagpadayon', 'sa', 'bakona', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,597 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'mando', 'ni', 'Catan', 'mao', 'ang', 'pag-ila', 'kon', 'kisan', 'sa', 'mga', 'bata', 'ang', 'dunay', 'history', 'sa', 'dengue', 'ug', 'wala', 'aron', 'paghimo', 'sa', 'tukma', 'nga', 'lakang', 'sa', 'pag-monitor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,598 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'Catan', 'nitambag', 'sa', 'publiko', 'nga', 'dili', 'mag-panic', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,599 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'taga', 'dakbayan', 'sa', 'Danao', ',', 'Cebu', 'ang', 'kalit', 'nahimong', 'milyonaryo', 'human', 'nakadaog', 'og', 'P29.7', 'million', 'jackpot', 'prize', 'sa', 'Grand', 'Lotto', '6', '/', '55', 'sa', 'bola', 'niadtong', 'Sabado', 'sa', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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