Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,700 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'sa', 'dakbayan', 'sa', 'Sugbo', ',', 'pipila', 'ka', 'mga', 'dapit', 'ang', 'nakasinati', 'og', 'brownout', 'human', 'nangatumba', 'ang', 'poste', 'ingon', 'man', 'natumbahan', 'kini', 'og', 'kahoy', 'tungod', 'sa', 'kusog', 'nga', 'hangin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,701 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Daanbantayan', 'Mayor', 'Vicente', 'Loot', 'nga', '3,725', 'ka', 'mga', 'pamilya', 'ang', 'gipabakwit', 'sa', 'mga', 'panimalay', 'nga', 'lig-on', 'samtang', 'ang', 'laing', '401', 'anaa', 'gidala', 'sa', 'ilang', 'gym', ',', 'barangay', 'halls', 'ug', 'tunghaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,702 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikompirmar', 'usab', 'ni', 'Loot', 'nga', 'aduna’y', 'parte', 'sa', 'provincial', 'road', 'sa', 'Barangay', 'Anapog', 'ang', 'nalusno', 'human', 'nihumok', 'sa', 'padayon', 'nga', 'pag-uwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,703 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'sa', 'mayor', 'nga', 'anaa', 'sa', '20', 'metros', 'ang', 'gitas-on', 'sa', 'maong', 'dalan', 'nga', 'nahulga', 'ug', 'kasamtangan', 'nga', 'dili', 'kini', 'maagian', 'og', 'sakyanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,704 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Tribunalo', 'nga', 'aduna’y', 'mga', 'nanga-stranded', 'nga', 'pasahero', 'sa', 'pantalan', 'sa', 'Hagnaya', 'sa', 'San', 'Remigio', 'nga', 'namasilong', 'una', 'sa', 'mga', 'panimalay', 'duol', 'sa', 'pantalan', 'nga', 'lig-on', 'ang', 'estraktura', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,705 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'lungsod', 'sa', 'Tabogon', ',', 'amihanang', 'Sugbo', ',', '51', 'ka', 'mga', 'pamilya', 'ang', 'gihiklin', 'ngadto', 'sa', 'mas', 'luwas', 'nga', 'lugar', 'gikan', 'sa', 'walo', 'ka', 'mga', '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, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,706 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpadayon', 'usab', 'og', 'hawan', 'ang', 'team', 'sa', 'Cebu', 'Electric', 'Cooperative', '(', 'Cebeco', ')', 'sa', 'apektadong', 'mga', 'lugar', 'ubos', 'sa', 'hurisdiksyon', 'niini', 'ingon', 'man', 'ang', 'clearing', 'operations', 'sa', 'mga', 'lungsod', 'sa', 'Carmen', ',', 'Borbon', 'ug', 'Sogod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0] | cebuaner |
5,707 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dunay', 'pipila', 'ka', 'mga', 'lugar', 'sa', 'mga', 'lungsod', 'sa', 'Tabuelan', ',', 'San', 'Remigio', 'ang', 'nakasinati', 'og', 'brownout', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0] | cebuaner |
5,708 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'sa', 'dakbayan', 'sa', 'Sugbo', ',', 'gikompirmar', 'usab', 'ni', 'Quennie', 'Bronce', ',', 'tigpamaba', 'sa', 'Visayan', 'Electric', 'Company', '(', 'VECO', ')', 'nga', 'nakasinati', 'og', 'brownout', 'ang', 'sagad', 'sa', 'bukirang', 'bahin', 'sa', 'syudad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,709 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alas', '3:00', 'sa', 'hapon', 'kagahapon', 'dihang', 'gibutyag', 'ni', 'Bronce', 'nga', 'aduna’y', 'pipila', 'ka', 'mga', 'barangay', 'sa', 'Cebu', 'City', 'ang', 'apektado', 'ang', 'linya', 'o', 'poste', 'niini', 'hinungdan', 'nga', 'wala', 'pa', 'hingpit', 'nabalik', 'ang', 'kuryente', '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, 1, 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] | cebuaner |
5,710 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nagsige', 'na', 'og', 'trabaho', 'ang', 'ilang', 'team', 'aron', 'mabalik', 'dayon', 'ang', 'kuryente', 'sa', 'pipila', 'ka', 'mga', 'dapit', 'sa', 'Busay', ',', 'Taptap', ',', 'ug', 'Lahug', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 0] | cebuaner |
5,711 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'usab', 'mga', 'punoan', 'sa', 'saging', 'ug', 'manga', 'ang', 'nangatumba', 'sa', 'mga', 'barangay', 'Sudlon', 'I', ',', 'Cambinocot', 'ug', 'Lusaran', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 5, 0] | cebuaner |
5,712 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'hinuon', 'naangol', 'sa', 'hitabo', 'samtang', 'wala', 'usab', 'madanyos', 'ang', 'simbahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,713 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bañacia', 'ug', 'mga', 'sakop', 'dali', 'nakapahigayon', 'og', 'clearing', 'operation', 'sa', 'mga', 'dapit', 'diin', 'dunay', 'mga', 'punoan', 'sa', 'kahoy', 'nga', 'nangatumba', 'ug', 'nakababag', 'sa', 'mga', '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, 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 |
5,714 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giputol', 'usab', 'nila', 'ang', 'mga', 'punoan', 'sa', 'kahoy', 'nga', 'padung', 'na', 'mabali', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,715 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'usab', 'gamay', 'nga', 'pagdahili', 'sa', 'yuta', 'ang', 'nahitabo', 'sa', 'Barangay', 'Sirao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,716 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Miyerkules', ',', 'gipaabot', 'mogawas', 'ang', 'bagyong', 'Urduja', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0] | cebuaner |
5,717 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'datus', 'sa', 'Cebu', 'Coast', 'Guard', 'pinaagi', 'sa', 'commander', 'niini', 'nga', 'si', 'Jerome', 'Cayabyab', ',', 'niabot', 'na', 'sa', '62', 'ka', 'mga', 'sakyanan', 'sa', 'kadagatan', 'ang', 'wala', 'nila', 'pabiyahia', 'lakip', 'na', 'usab', 'ang', 'siyam', 'ka', 'mga', 'cargo', 'vessel', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 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] | cebuaner |
5,718 | 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', 'biyahe', 'nga', 'kanselado', 'ang', 'gikan', 'sa', 'pipila', 'ka', 'pantalan', 'sa', 'Sugbo', 'paingon', 'sa', 'Leyte', ',', 'Bohol', ',', 'Negros', 'Occidental', ',', 'Masbate', ',', 'Siquijor', ',', 'Cagayan', '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, 5, 0, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0] | cebuaner |
5,719 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Taliwa', 'sa', 'wala', 'ug', 'tuo', 'nga', 'mga', 'Christmas', 'party', ',', 'si', 'Cebu', 'Archbishop', 'Jose', 'Palma', 'nagpahinumdom', 'sa', 'katawhan', 'sa', 'tinuod', 'nga', 'kahulogan', 'sa', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,720 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'arsobispo', 'maoy', 'nangu', 'sa', 'pagbukas', 'sa', 'siyam', 'ka', 'adlaw', 'nga', 'Misa', 'de', 'Gallo', 'sa', 'bag-ong', 'gitukod', 'nga', 'simbahan', ',', 'ang', 'San', 'Isidro', 'Labrador', 'Parish', 'sa', 'Barangay', 'Garing', ',', 'lungsod', 'sa', 'Consolacion', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 3, 4, 4, 4, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
5,721 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'wali', ',', 'si', 'Palma', 'nipasabot', 'sa', 'kamahinungdanon', 'sa', 'Misa', 'de', 'Gallo', 'nga', 'mao', 'ang', 'pagpangandam', 'sa', 'pag-abot', 'ug', 'pagkatawo', 'ni', 'Hesukristo', 'sa', 'kalibotan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 0, 0, 0] | cebuaner |
5,722 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'dili', 'angay', 'kalimtan', 'sa', 'mga', 'tawo', 'nga', 'bisan', 'sa', 'daghang', 'mga', 'Christmas', 'party', ',', 'ang', 'sentro', 'sa', 'kasaulogan', 'mao', 'ang', 'pagkatawo', 'sa', 'Ginuo', 'ug', 'angay', 'kini', 'nga', 'padayunon', 'sa', 'matag', 'pamilya', 'sa', 'panimalay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,723 | 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', 'bisag', 'gamay', 'nga', 'imahe', 'sa', 'Señor', 'Santo', 'Niño', 'ibutang', 'sa', 'altar', 'aron', 'magpahinumdom', 'sa', 'gugma', 'sa', 'Ginuo', 'sa', 'mga', 'tawo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0] | cebuaner |
5,724 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nalipay', 'sa', 'bag-ong', 'simbahan', 'nga', 'gipabarog', 'sa', 'Consolacion', 'nga', 'bisan', 'wala', 'pa', 'mahuman', 'og', 'tukod', ',', 'nidagsa', 'ang', 'mga', 'tawo', 'sa', 'unang', 'adlaw', 'sa', 'Misa', 'de', 'Gallo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,725 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Padre', 'Cola', 'mapasalamaton', 'ni', 'Palma', 'nga', 'bisan', 'og', 'dunay', 'bagyong', 'Urduja', 'niadto', 'sa', 'ilang', '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, 1, 0, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0] | cebuaner |
5,726 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'bisan', 'og', 'piso', 'way', 'nabayran', 'ang', 'parokya', 'sa', 'pagpalit', 'sa', 'yuta', 'ug', 'pagpatag', 'niini', 'nga', 'nahimutang', 'sa', 'bukirang', 'bahin', 'sa', 'Barangay', 'Garing', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 6, 0] | cebuaner |
5,727 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nibutyag', 'nga', 'usa', 'ka', 'architect', 'ug', 'engineer', 'nga', 'taga', 'Quezon', 'Province', 'ug', 'tua', 'na', 'nagpuyo', 'sa', 'Germany', 'ang', 'nipalit', 'sa', 'yuta', 'og', 'dul-an', 'P1', 'milyon', 'ug', 'naimpas', 'og', 'bayad', 'sud', 'sa', '26', 'ka', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,728 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nilungtad', 'og', 'siyam', 'ka', 'buwan', 'ang', 'paghawan', 'ug', 'pag-ugmad', 'sa', 'yuta', 'sa', 'lugar', 'tungod', 'sa', 'kabukid', 'sa', 'nahimutangan', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,729 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'usab', 'sila', 'gipabayad', 'sa', 'pag-develop', 'sa', 'yuta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,730 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Cola', 'nidugang', 'ang', 'David', 'Justin', 'Heavy', 'Equipment', 'Corp.', ',', 'ang', 'libre', 'nagpagamit', 'sa', 'crane', 'ug', 'pison', 'alang', 'sa', 'pagbutang', 'og', 'mga', 'haligi', 'ug', 'trusses', 'sa', 'simbahan', 'nga', 'gisugdan', 'og', 'tukod', 'niadtong', 'Hunyo', '3', '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, 1, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,731 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aron', 'mahuman', 'ang', 'katukoran', 'sa', 'simbahan', ',', 'nagpatabang', 'sila', 'sa', 'ubang', 'mga', 'parokya', 'pinaagi', 'sa', 'second', 'collection', 'labi', 'na', 'sa', 'P3.7', 'milyones', 'nga', 'gisugyot', 'nga', 'altar', 'retablo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,732 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Consolacion', 'Mayor', 'Teresa', 'Alegado', 'nitabang', 'usab', 'sa', 'fundraising', 'alang', 'sa', 'proyekto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,733 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Bihag', 'nga', 'sugdan', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'ang', 'nightly', 'activities', 'nga', 'karong', 'Lunes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
5,734 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paambit', 'niya', 'nga', 'anaa', 'sa', 'P1', 'milyones', 'ang', 'gitagana', 'sa', 'dakbayan', 'alang', 'sa', 'mga', 'kalihukan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,735 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipatawag', 'sa', 'direktor', 'sa', 'PRO', '7', 'ang', 'tanang', 'hepe', 'sa', 'police', 'stations', 'apil', 'ang', 'ilang', 'personnel', 'nga', 'sakop', 'sa', 'DEU', 'alang', 'na', 'sa', 'pagbalik', 'sa', 'ilang', 'anti-drug', 'operation', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,736 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'gibutyag', 'sa', 'information', 'officer', 'sa', 'PRO', '7', ',', 'Supt.', 'Reyman', 'Tolentin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
5,737 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'katuyoan', 'niini', 'mao', 'ang', 'pagsuta', 'sa', 'bag-ong', 'guidelines', 'sa', 'kampo', 'Crame', 'may', 'kalabotan', 'sa', 'gubat', 'batok', 'sa', 'ilegal', 'nga', 'drugas', 'nga', 'wala', 'nila', 'himoa', 'sa', 'nanglabay', 'nga', 'operasyon', ',', 'matod', 'ni', 'Tolentin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,738 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'ni', 'Tolentin', 'nga', 'dili', 'tugotan', 'nga', 'molusad', 'og', 'anti', '-drug', 'operation', 'ang', 'usa', 'ka', 'pulis', 'nga', 'dunay', 'sektetong', 'ilegal', 'nga', 'binuhatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,739 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Atong', 'gipanghingusgan', 'sad', 'ang', 'atoang', 'internal', 'cleansing', 'aron', 'pagsiguro', 'gyud', 'nga', 'ang', 'miyembro', 'sa', 'anti-drug', 'operation', 'dili', 'miyembro', 'sa', 'pikas', 'ug', 'pagsiguro', 'nga', 'tinud-anay', 'ang', 'pag', 'serbisyo', ',', '”', 'matod', 'ni', 'Tolentin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0] | cebuaner |
5,740 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'usab', 'sa', 'kamandoan', 'sa', 'Kampo', 'Crame', 'ang', 'pagpasul-ob', 'sa', 'body', 'cameras', 'sa', 'mga', 'pulis', 'panahon', 'sa', 'ilang', 'operasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,741 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tinguha', 'nila', 'nga', 'ang', 'tanan', 'niyang', 'sakop', 'walay', 'bahid', 'sa', 'kurapsyon', 'ug', 'wa', 'nalambigit', 'sa', 'ilegal', 'nga', 'kalihukan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,742 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'mga', 'tumong', ',', 'matod', 'ni', 'Navida', ',', 'malikayan', 'ang', 'mga', 'pagduda', 'nga', 'wala', 'mausab', 'ang', 'kapulisan', 'sa', 'ilang', 'internal', 'cleansing', 'ug', 'nagpabilin', 'ang', 'problema', 'sa', 'kurapsyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,743 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gimando', 'sa', 'Provincial', 'Veterinary', 'Office', '(', 'PVO', ')', 'ang', 'paghingusog', 'sa', 'pagpatuman', 'sa', 'mga', 'lagda', 'alang', 'sa', 'pagbaligya', 'og', 'luwas', 'nga', 'karne', 'sa', 'kamerkadohan', 'sa', 'probinsiya', ',', 'ilabi', 'na', 'niining', 'Christmas', 'season', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 4, 4, 0, 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 |
5,744 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dunay', 'mga', 'tinugyanan', 'sa', 'PVO', 'nga', 'niabag', 'karon', 'sa', 'National', 'Meat', 'Inspection', 'Service', '(', 'NMIS', ')', 'sa', 'ilang', 'pagsubay', 'sa', 'mga', 'merkado', 'ug', 'bisan', 'sa', 'mga', 'mall', 'sa', 'ilang', 'pagsunod', 'sa', 'insaktong', 'proseso', 'sa', 'meat', 'handling', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,745 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'holiday', 'season', ',', 'taas', 'ang', 'demand', 'sa', 'karne', 'karon', 'sa', 'kamerkadohan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,746 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Provincial', 'Veterinarian', 'Doctor', 'Mary', 'Rose', 'Vincoy', ',', 'dili', 'problema', 'sa', 'Sugbo', 'ang', 'double', 'dead', 'nga', 'mga', 'karne', 'tungod', 'sa', 'daghang', 'local', 'supply', '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, 2, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,747 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', ',', 'nag-unang', 'suliran', 'dinhi', 'ang', 'mishandling', 'sa', 'karne', 'ug', 'ang', 'uban', 'walay', 'meat', 'inspection', 'certificate', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,748 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigun', 'ni', 'Vincoy', ',', 'kinahanglang', 'sutaon', 'una', 'sa', 'mga', 'konsumidor', 'kon', 'limpyo', 'ug', 'luwas', 'ang', 'karne', 'nga', 'paliton', 'pinaagi', 'sa', 'pagpangita', 'sa', 'tatak', 'nga', 'magmatuod', 'nga', 'nipaubos', 'kini', 'sa', 'insaktong', 'meat', 'inspection', 'gikan', 'sa', 'pag-ihaw', 'hangtud', 'sa', 'pagdala', 'sa', 'merkado', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,749 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'masakpan', 'nga', 'mag-display', 'ug', 'mishandled', 'o', 'kaha', 'wala', 'mainspeksiyon', 'nga', 'karne', ',', 'mag-atubang', 'og', 'administrative', 'sanction', 'gikan', 'sa', 'NMIS', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 3, 0] | cebuaner |
5,750 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibalo', 'ni', 'Vincoy', 'nga', 'na-establisar', 'na', 'isip', 'bag-ong', 'dibisyon', 'sa', 'iyang', 'opisina', 'ang', 'Provincial', 'Meat', 'Inspection', 'Service', '(', 'PMIS', ')', ',', 'diin', 'ubos', 'sa', 'hurisdiksiyon', 'niini', 'ang', 'tanang', 'merkado', ',', 'mall', ',', 'meat', 'shop', ',', 'poultry', 'dressing', 'plants', 'ug', 'ihawan', 'sa', 'probinsiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,751 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Vincoy', ',', 'sama', 'sa', 'NMIS', ',', 'ang', 'ilang', 'trained', 'personnel', 'na', 'ang', 'modakop', 'sa', 'mga', 'malapason', 'sa', 'mga', 'meat', 'inspection', 'guidelines', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,752 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'vintage', 'bomb', 'ang', 'napalgan', 'kagahapon', 'sa', 'tungang', 'gabii', 'sa', 'usa', 'ka', 'construction', 'site', 'sa', 'Barangay', 'Lahug', ',', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
5,753 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Mabolo', 'Police', 'Station', 'nakadawat', 'sa', 'maong', 'alarma', 'diin', 'anaa', 'sa', '2,000', 'pounds', 'ang', 'gibug-aton', '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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,754 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alas', '12:00', 'sa', 'tungang', 'gabii', 'kini', 'nakit-an', 'samtang', 'dali', 'kini', 'nga', 'gi-turn-over', 'sa', 'Explosive', 'and', 'Ordnance', 'Disposal', '(', 'EOD', ')', 'sa', 'Swat-CCPO', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 4, 4, 4, 4, 0, 3, 0] | cebuaner |
5,755 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'gipaabot', 'makagawas', 'sa', 'nasod', 'ang', 'bagyong', 'Urduja', ',', 'laing', 'potensyal', 'nga', 'bagyo', 'ang', 'mosud', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', 'ug', 'magdala', 'og', 'ulan', 'sa', 'Sugbo', 'nga', 'duol', 'na', 'sa', 'adlaw', 'sa', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 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] | cebuaner |
5,756 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nipasabot', 'ang', 'LPA', 'mahimo', 'na', 'nga', 'tropical', 'depression', 'sa', 'dili', 'pa', 'makasud', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', 'samtang', 'ang', 'bagyong', 'Urduja', 'nana', 'sa', 'PAR', 'mao', 'pay', 'pagkahimo', 'nga', 'Tropical', 'Depression', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,757 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'niklaro', 'nga', 'daghan', 'pa', 'og', 'mahitabo', 'sa', 'LPA', 'nga', 'mahimo', 'moliko', 'o', 'mahilis', 'base', 'sa', '23', 'ka', 'modelo', 'nga', 'ilang', 'gitun-an', 'ug', 'gibantayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,758 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'duna', 'usab', 'posibilidad', 'nga', 'mahimo', 'kini', 'nga', 'severe', 'tropical', 'storm', 'nga', 'magdala', 'og', 'hangin', 'nga', 'may', 'gikusgon', 'nga', 'moabot', 'hangtod', 'sa', '120', 'ka', 'kilometros', 'ngadto', 'sa', '130', 'ka', 'kilometros', 'matag', 'takna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,759 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'makamugna', 'na', 'og', 'storm', 'surge', ',', 'daghang', 'matumba', 'nga', 'mga', 'kahoy', 'ug', 'mapadpad', 'nga', 'mga', 'balay', 'nga', 'gama', 'sa', 'light', 'materials', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,760 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Tabada', 'nidugang', 'kon', 'basehanan', 'ang', 'tulo', 'ka', 'models', 'ang', 'bagyong', 'Vinta', 'matungod', 'sa', 'Kabisay-an', 'sa', 'Disyembre', '23', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,761 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Taliwa', 'sa', 'hulga', ',', 'ang', 'kapulisan', 'sa', 'Sugbo', ',', 'labina', 'ang', 'naa', 'sa', 'amihanang', 'bahin', 'nga', 'mas', 'apektado', ',', 'gimandoan', 'sa', 'pagbantay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,762 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', ',', 'matod', 'niya', ',', 'masinati', 'ang', 'kusog', 'pa', 'nga', 'pag-ulan', 'apan', 'ugma', ',', 'hinay', 'ngadto', 'sa', 'kasarangan', 'na', ',', 'samtang', 'sa', 'Martes', 'makita', 'na', 'ang', 'adlaw', 'apan', 'dunay', 'gamay', 'nga', 'pag-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, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,763 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagkutlo', 'ning', 'balita', ',', 'ang', 'bagyo', 'katapusan', 'nakit-an', 'sa', 'gilay-on', 'nga', '205', 'kilometros', 'sa', 'silangan', 'sa', 'Borongan', 'City', ',', 'Eastern', 'Samar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 6, 6, 6, 6, 0] | cebuaner |
5,764 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Tabada', 'niingon', 'nga', 'tungod', 'sa', 'frontal', 'system', ',', 'ridge', 'of', 'high', 'pressure', 'area', 'nga', 'naa', 'sa', 'taas', 'sa', 'latitude', ',', 'hinay', 'nga', 'nakasaka', 'ang', 'bagyong', 'Urduja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
5,765 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Kini', 'maoy', 'nakapugong', 'nga', 'mosaka', 'ang', 'bagyo', 'kay', 'iduso', 'na', 'siya', 'maayo', 'sa', 'ubos', ',', 'nagtuyok-tuyok', 'pa', 'na', 'siya', ',', 'gasuroysuroy', ',', '”', 'sumala', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,766 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Urduja', 'nagdala', 'sa', 'hangin', 'nga', 'may', 'gikusgon', 'nga', 'moabot', 'ngadto', 'sa', '75', 'ka', 'kilometros', 'matag', 'oras', 'duol', 'sa', 'iyang', 'sentro', 'ug', 'pag-unos', 'nga', 'moabot', 'ngadto', 'sa', '90', 'ka', 'kilometros', 'matag', 'takna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 7, 0, 0, 0, 0, 0, 0, 0, 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 |
5,767 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'bagyong', 'Urduja', ',', 'misaka', 'na', 'sa', '33', 'ka', 'mga', 'sakyanan', 'sa', 'kadagatan', 'ang', 'wa', 'makabiyahe', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,768 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Coast', 'Guard', 'Station', 'Cebu', 'Commander', 'Jerome', 'Cayabyab', 'nibutyag', 'nga', 'apil', 'sa', 'mga', 'biyahe', 'nga', 'nakansilar', 'ang', 'mga', 'barko', ',', 'fastcraft', ',', 'ug', 'motor', 'banca', 'nga', 'padung', 'sa', 'Ormoc', ',', 'Maasin', ',', 'Bohol', ',', 'mga', 'isla', 'sa', 'Camotes', 'ug', 'Bantayan', 'ug', 'ubang', 'bahin', '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, 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, 5, 0, 5, 0, 5, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
5,769 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'nay', 'request', 'sa', 'pagpaayo', '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] | cebuaner |
5,770 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sukad', 'pa', 'niadtong', 'miaging', 'adlaw', 'nasinati', 'na', 'sa', 'tibuok', 'Sugbo', 'ang', '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, 5, 0, 0, 0] | cebuaner |
5,771 | 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', 'traffic', 'light', 'ug', 'signs', 'ang', 'nangaguba', 'nga', 'nakaapekto', 'sab', 'sa', 'dagan', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,772 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'ni', 'Sarador', ',', 'nangaguba', 'kini', 'tungod', 'sa', 'lainlaing', 'mga', 'rason', 'sama', 'sa', 'traffic', 'light', 'sa', 'M.J.', 'Cuenco', 'avenue', 'nga', 'nasunog', 'apan', 'di', 'sab', 'sila', 'dayon', 'kapalit', 'og', 'bag-ong', 'piyesa', 'kay', 'gikan', 'sa', 'gawas', 'sa', 'nasod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,773 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisulusyonan', 'sab', 'sa', 'CCTO', 'ang', 'maong', 'problema', 'pinaagi', 'sa', 'pagdestino', 'og', 'mga', 'mga', 'traffic', 'enforcer', 'nga', 'maoy', 'modumala', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,774 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Naa', 'mi', 'plano', 'nga', 'pasigaon', 'og', 'balik', '(', 'traffic', 'lights', ')', 'pero', 'di', 'pa', 'mi', 'kaingon', 'og', 'kanus-a', ',', '”', 'matod', 'pa', 'ni', 'Sarador', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,775 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moduso', 'pa', 'sab', 'og', 'budget', 'ang', 'planning', 'section', 'sa', 'CCTO', 'aron', 'malihok', 'na', 'ang', 'pag-ilis', 'sa', 'mga', 'nagubang', 'traffic', 'lights', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,776 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ernesto', 'Morales', 'Jr.', ',', 'engineering', 'assistant', ',', 'naghangyo', 'sa', 'mga', 'motorista', 'ug', 'publiko', 'nga', 'di', 'kawaton', ',', 'suwat-suwatan', ',', 'ug', 'dasmagan', 'ang', 'mga', 'signages', 'nga', 'nahimutang', 'sa', 'mga', 'center', 'island', 'ug', 'daplin', 'sa', 'mga', 'kalsada', 'aron', 'mas', 'han-ay', 'ang', 'dagan', 'sa', 'trapiko', 'ug', 'sa', 'paglikay', 'sab', 'sa', 'mga', 'disgrasya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,777 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Garcia', ',', 'ang', 'Department', 'of', 'Health', 'una', 'nang', 'nitan-aw', 'nga', 'iapil', 'ang', 'Sugbo', 'gumikan', 'kay', 'nagkuha', 'dinhi', 'og', 'samplings', 'ug', 'tungod', 'sa', 'kataas', 'sa', 'percentage', 'sa', 'pagsaka', 'sa', 'mga', 'insidente', 'sa', 'dengue', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,778 | 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', 'dili', 'lang', 'siya', 'ang', 'nihangyo', 'sa', 'DOH', 'nga', 'apilon', 'ang', 'rehiyon', '7', 'tungod', 'kay', 'ang', 'ubang', 'mga', 'kongresista', 'nihangyo', 'usab', 'nga', 'ilakip', 'ang', 'ilang', 'lugar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,779 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ubial', 'unang', 'nipasangil', 'nga', 'napugos', 'siya', 'nga', 'ipadayon', 'ang', 'pagpamakuna', 'sa', 'Dengvaxia', 'vaccine', 'kay', 'gi-pressure', 'siya', 'og', 'pipila', 'ka', 'mga', 'kongresista', 'ug', 'wala', 'ma-confirm', 'sa', 'Commission', 'on', 'Appointments', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0] | cebuaner |
5,780 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahibung', 'usab', 'ang', 'kanhi', 'gobernador', 'sa', 'Sugbo', 'kon', 'nganong', 'siya', 'lang', 'ug', 'si', 'Iloilo', '1st', 'District', 'Rep.', 'Richard', 'Garin', ',', 'bana', 'ni', 'kanhi', 'Health', 'Secretary', 'Janette', 'Garin', ',', 'ang', 'ginganlan', 'ni', 'Ubial', 'nga', 'giingong', 'ni-pressure', 'niini', 'nga', 'ipadayon', 'ug', 'palapdan', 'ang', 'vaccination', 'program', 'nga', 'bisan', 'si', 'Presidential', 'spokesperson', 'Harry', 'Roque', 'ang', 'usa', 'usab', 'sa', 'mga', 'vocal', 'batok', 'sa', 'programa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 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, 0] | cebuaner |
5,781 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nipasabot', 'si', 'Radaza', 'nga', 'sagad', 'sa', 'mga', 'tinun-an', 'duol', 'ra', 'ang', 'ilang', 'pinuy-anan', 'sa', 'ilang', 'tunghaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,782 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Supt.', 'Artemio', 'Recabo', 'nga', 'dunay', 'mga', 'gipang', 'montar', 'nga', 'mga', 'police', 'assistance', 'desk', 'sa', 'matag', 'simbahan', 'lakip', 'na', 'ang', 'mga', 'unipormadong', 'police', 'personnel', 'isip', 'beat', 'patrol', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0] | cebuaner |
5,783 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ipakatap', 'usab', 'ang', 'mga', 'mobile', 'patrol', 'group', 'nga', 'maoy', 'mo', 'responde', 'kung', 'dunay', 'mga', 'alarma', 'nga', 'mahitabo', 'sa', 'tibuok', 'siyudad', 'sa', 'Sugbo', 'ug', 'mo', 'patrolya', 'sa', 'kadalanan', 'aron', 'mahimong', 'luwas', 'ang', 'mga', 'tawo', 'nga', 'nagbaktas', 'paingon', 'sa', 'simbahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,784 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Awhag', 'niya', 'sa', 'publiko', 'nga', 'sa', 'panahon', 'nga', 'mosimba', 'sa', 'misa', 'de', 'gallo', 'dili', 'magdala', 'ug', 'mahalong', 'butang', 'ug', 'daghang', 'kwarta', 'aron', 'dili', 'mabiktima', 'sa', 'mga', 'kriminal', 'nga', 'mosakay', 'sa', 'panahon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,785 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alerto', 'sa', 'kanunay', 'ug', 'kung', 'adunay', 'mamatikdan', 'nga', 'dautang', 'lihok', 'labing', 'maayo', 'nga', 'ireport', 'dayon', 'sa', 'pulis', 'nga', 'nadestino', 'sa', 'sulod', 'sa', 'simbahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,786 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Atung', 'advice', 'nila', 'nga', 'dili', 'sila', 'magdala', 'ug', 'mahalon', 'nga', 'butang', 'ug', 'daghang', 'kwarta', 'kay', 'wala', 'ta', 'kahibalo', 'nga', 'sila', 'ra', 'ang', 'gibantayan', 'sa', 'mga', 'kawatan', ',', '”', 'matod', 'ni', 'Recabo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0] | cebuaner |
5,787 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'mga', 'police', 'ang', 'kagamhanan', 'sa', 'siyudad', 'sa', 'Sugbo', 'mopakatap', 'usab', 'ug', 'mga', 'ambulansya', 'gikan', 'sa', 'kabarangayan', 'alang', 'sa', 'mga', 'nanimba', 'nga', 'nakasinati', 'ug', 'kalipong', 'sa', 'suod', 'sa', 'simbahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,788 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Police', 'Regional', 'Office', '7', 'mosangon', 'sa', 'mga', 'Chief', 'of', 'Police', 'nga', 'mohatag', 'ug', 'mensahe', 'sa', 'mga', 'simbahan', 'atol', 'sa', 'misa', 'de', 'gallo', 'kini', 'tipik', 'sa', 'programa', 'sa', 'kapulisan', 'nga', 'transformation', 'program', 'ug', 'mohatag', 'ug', 'advice', 'nga', 'magbantay', 'sa', 'mga', 'kriminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
5,789 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidasig', 'usab', 'sa', 'PRO-7', 'nga', 'ilakip', 'sa', 'mensahe', 'sa', 'mga', 'police', 'atubangan', 'sa', 'mga', 'manimbahay', 'ang', 'pagsunod', 'sa', 'espiritohanon', 'nga', 'aspeto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,790 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gimandoan', 'na', 'usab', 'sa', 'PRO-7', 'Director', 'ang', 'tanang', 'police', 'stations', 'lukop', 'sa', 'rehiyon', 'nga', 'isaka', 'ang', 'alert', 'status', 'sa', 'unang', 'adlaw', 'sa', 'misa', 'de', 'gallo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,791 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikalipay', 'ang', 'sayo', 'nga', 'paghatag', 'sa', 'katapusang', 'hugna', 'sa', 'financial', 'assitance', 'nga', 'P2,000', 'alang', 'sa', 'senior', 'citizens', 'sa', 'dakbayan', 'sa', 'Sugbo', '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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
5,792 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugdan', 'kini', 'sa', 'alas', '8:00', 'sa', 'buntag', 'hangtod', 'sa', 'alas', '12:00', 'sa', 'udto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,793 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'sab', 'ni', 'Chavez', 'nga', 'adunay', '2,000', 'kapin', 'ang', 'mga', 'bed-ridden', 'nga', 'kinahanglan', 'gyud', 'ipadeliber', 'kini', 'sa', 'matag', 'balay', '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, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,794 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'matag', 'benepisyaryo', 'makadawat', 'og', 'P2,000', 'para', 'kini', 'sa', 'tibuok', 'Nobiyembre', 'ug', 'Disyembre', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,795 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Midagan', 'sa', '67,300', 'ang', 'total', 'sa', 'mga', 'kuwalipikado', 'nga', 'makadawat', 'nga', 'senior', 'citizens', 'karong', 'Dominggo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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 |
5,796 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nihangyo', 'sad', 'si', 'Chavez', 'nga', 'magpamiyembro', 'gyud', 'sila', 'sa', 'sa', 'asosasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,797 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigun', 'sa', 'Provincial', 'Tourism', 'Office', '(', 'PTO', ')', ',', 'buot', 'nila', 'nga', 'madani', 'ang', 'mga', 'millennial', ',', 'lokal', 'o', 'langyaw', 'nga', 'turista', ',', 'pinaagi', 'sa', 'ilang', 'mga', '“adventure', 'destinations”', ',', 'nga', 'gidugang', 'sa', 'naandan', 'nga', 'mga', 'destinasyon', 'sa', 'kalihukan', '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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,798 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sulod', 'sa', 'pipila', 'ka', 'tuig', ',', 'giulohan', 'sa', 'Kapitolyo', 'ug', 'Southern', 'Getaway', 'o', 'Heritage', 'Tour', 'ang', 'ilang', 'pagpahigayon', 'ug', 'Suroy-suroy', 'Sugbo', 'sa', 'southern', 'Cebu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 3, 4, 0, 0, 0, 0, 3, 4, 0, 5, 6, 0] | cebuaner |
5,799 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'sa', 'Enero', '2018', ',', 'gipahibalo', 'sa', 'PTO', 'nga', '“Adventure', 'Round', 'South”', 'na', 'ang', 'ilang', 'giulohan', 'niini', 'tungod', 'sa', 'dugang', 'destinasyon', 'nga', 'alang', 'sa', 'mga', 'adventure-seekers', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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