Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,700 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'DOH', 'nipalit', 'sa', 'Dengvaxia', 'alang', 'sa', 'usa', 'ka', 'milyon', 'ka', 'mga', 'bata', 'sa', 'publikong', 'mga', 'tunghaan', 'sa', 'mga', 'dapit', 'diin', 'taas', 'ang', 'mga', 'kaso', 'sa', 'dengue', 'sa', '2015', ':', 'National', 'Capital', 'Region', ',', 'Region', '3', ',', 'Region', '4A', 'ug', 'Central', 'Visayas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 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, 5, 6, 6, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0] | cebuaner |
6,701 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'rehiyon', ',', 'ang', 'bakuna', 'nasugdan', 'na', 'og', 'hatag', 'ngadto', 'sa', 'mga', 'bata', 'nga', 'nagpangidaron', 'og', '9', 'hangtod', 'sa', '14-anyos', 'sa', 'Sugbo', 'diin', 'ang', 'mga', 'siyudad', 'sa', 'Mandaue', 'ug', 'Lapu-Lapu', 'nga', 'maoy', 'unang', 'nakahatag', 'sa', 'unang', 'hugna', 'sa', 'bakuna', 'niadtong', 'Hunyo', '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, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,702 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mayor', 'nibutyag', 'nga', 'nakadawat', 'siya', 'og', 'mga', 'reklamo', 'gikan', 'sa', 'mga', 'private', 'car', 'owner', 'sanglit', 'ang', 'mga', 'karsada', 'nga', 'maoy', 'ilang', 'agian', 'kasagaran', 'gubaon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,703 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'gidugayon', 'maoy', 'paghatag', 'og', 'panahon', 'sa', 'mga', 'personnel', 'sa', 'Department', 'of', 'Engineering', 'and', 'Public', 'Works', '(', 'DEPW', ')', 'nga', 'maaspalto', 'ang', 'maong', 'mga', 'karsada', 'sanglit', 'gawas', 'nga', 'adunay', 'daghang', 'libaong', ',', 'abog', 'usab', 'kaayo', 'ang', '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, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,704 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mayor', 'gusto', 'nga', 'i-ban', 'ang', 'mga', 'private', 'car', 'sanglit', 'numero', 'uno', 'kining', 'nakamugna', 'og', 'kahuot', 'sa', 'trapiko', 'sanglit', 'molagbas', 'ang', 'ilang', 'sakyanan', 'nga', 'gamay', 'ra', 'og', 'sakay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,705 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitun-an', 'na', 'karon', 'sa', 'duha', 'ka', 'mga', 'service', 'contractors', 'kon', 'posible', 'bang', 'makabutang', 'og', 'wind', 'turbine', 'power', 'plant', '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, 5, 0] | cebuaner |
6,706 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'kini', 'posible', ',', 'gawas', 'nga', 'kini', 'ang', 'labing', 'una', 'sa', 'Central', 'Visayas', ',', 'posible', 'usab', 'kini', 'nga', 'usa', 'ka', 'tourist', 'attraction', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,707 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'sa', 'tigpamaba', 'sa', 'Department', 'of', 'Energy', '(', 'DOE', ')', '-', 'Visayas', 'nga', 'si', 'Lourdes', 'Arciaga', 'nga', 'na-award', 'na', 'sa', 'duha', 'ka', 'kompaniya', 'ang', 'duha', 'ka', 'service', 'contracts', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,708 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Susama', 'sa', 'Bangui', 'Wind', 'Farm', 'sa', 'Ilocos', 'Norte', 'nga', 'gawas', 'sa', 'naghatag', 'kini', 'og', 'supply', 'sa', 'kuryente', ',', 'usa', 'usab', 'kini', 'sa', 'gibisita', 'sa', 'turista', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,709 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Arciaga', 'nga', 'posibleng', 'susama', 'usab', 'kini', 'sa', 'Sugbo', 'apan', 'sa', 'bukid', 'lang', 'nga', 'parte', 'diin', 'anaa', 'sa', 'bahin', 'sa', 'Trans-axial', 'highway', '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, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0] | cebuaner |
6,710 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'duha', 'gilauman', 'nga', 'mohatag', 'og', '741', 'megawatts', 'nga', 'supply', 'sa', 'kuryente', 'pinaagi', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,711 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Magdugang', 'kini', 'og', 'supply', 'sa', 'kuryente', 'sa', 'Sugbo', ',', 'apan', 'ning', 'higayuna', 'renewable', 'energy', 'ang', 'gigamit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
6,712 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugod', 'karong', 'buwana', ',', 'mopagawas', 'ang', 'Bangko', 'Sentral', 'ng', 'Pilipinas', 'og', 'bag-ong', 'P5', 'nga', 'mga', 'sensilyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,713 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'New', 'Generation', 'Currency', '(', 'NGC', ')', 'nga', 'P5', 'magpakita', 'sa', 'nawong', 'sa', 'bayani', 'nga', 'si', 'Andres', 'Bonifacio', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,714 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pagpagawas', 'sa', 'bag-ong', 'P5', 'nga', 'sensilyo', 'pahinungod', 'sa', 'ika-154', 'nga', 'birthday', 'anniversary', 'sa', 'bayani', 'niadtong', 'Nobiyembre', '30', 'ug', 'sa', 'iyang', 'ika-120', 'nga', 'anibersaryo', 'sa', 'kamatayon', 'niadtong', 'Mayo', '10', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 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 |
6,715 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bonifacio', ',', 'usa', 'ka', 'maisog', 'nga', 'lider', 'ug', 'usa', 'sa', 'mga', 'nagtukod', 'sa', 'Katipunan', ',', 'usa', 'ka', 'sikreto', 'nga', 'organisasyon', 'hangtod', 'sa', 'pagkadiskobri', 'niini', 'niadtong', '1896', 'nga', 'nisangko', 'sa', 'Philippine', 'Revolution', 'ug', 'sa', 'deklarasyon', 'sa', 'kaugalingnan', 'sa', 'Pilipinas', 'niadtong', '1898.', 'Si', 'Bonifacio', 'namatay', 'niadtong', 'Mayo', '10', ',', '1897', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,716 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'BSP', 'ang', 'ubang', 'denomination', 'sa', 'NGC', 'ipagawas', 'sa', 'sirkulasyon', 'sa', 'Enero', 'sa', 'sunod', '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, 3, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,717 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pikas', 'bahin', 'sa', 'sensiyo', ',', 'makita', 'ang', 'Tayabak', ',', 'usa', 'ka', 'klase', 'sa', 'endemic', 'nga', 'tanom', 'sa', 'Pilipinas', 'nga', 'makita', 'lang', 'sa', 'usa', 'ka', 'lugar', 'ug', 'mopilit', 'og', 'tag-as', 'nga', 'mga', 'punoan', 'sa', 'kahoy', 'sa', 'lasang', ';', 'ang', 'logo', 'sa', 'BSP', 'ug', 'ang', '“Bangko', 'Sentral', 'ng', 'Pilipinas”', 'sa', 'microprint', 'nga', 'marka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0] | cebuaner |
6,718 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'bag-ong', 'P5', 'NGC', 'coin', 'kay', '25', 'millimeters', 'ang', 'sukod', 'sa', 'dayametro', 'ug', 'motimbang', 'og', 'kapin', 'kun', 'kulang', '7.4', 'gramos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0] | cebuaner |
6,719 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'BSP', 'nidugang', 'ang', 'ubang', 'mga', 'sensilyo', 'sa', 'NGC', 'Series', 'mag-feature', 'usab', 'sa', 'logo', 'sa', 'BSP', ',', 'mga', 'bayani', 'ug', 'endemic', 'flora', 'uban', 'sa', 'desinyo', 'sa', 'NGC', 'Banknote', 'Series', 'nga', 'gilusad', 'niadtong', '2010', 'nga', 'nag-highlight', 'usab', 'sa', 'kahayupan', '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, 3, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,720 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Ang', 'operator', 'nahibawo', 'sa', 'drayber', 'ug', 'nakaila', 'kon', 'unsa', 'kini', 'matanga', 'sa', 'pagkadrayber', ',', '”', 'matod', 'ni', 'Tumulak', 'sa', 'pakighinabi', 'sa', 'Superbalita', 'Cebu', 'kagahapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 4, 0, 0] | cebuaner |
6,721 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'representante', 'sa', 'Gilgal', 'Taxi', 'nga', 'wa', 'magpahingan', 'niingon', 'nga', 'tubagon', 'sa', 'kompaniya', 'ang', 'pasangil', 'batok', 'sa', 'ilang', 'drayber', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0] | cebuaner |
6,722 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'wala', 'pagpakita', 'sa', 'drayber', 'nga', 'si', 'Jack', 'Espanto', 'nga', 'nagmaneho', 'sa', 'taxi', '(', 'AAE', '5719', ')', ',', 'matod', 'ni', 'Tumulak', ',', 'makonsiderar', 'nga', 'sad-an', 'kini', 'sa', 'reklamo', 'batok', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,723 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Tumulak', 'nitug-an', 'mag-', 'atubang', 'og', 'multa', 'nga', 'P8,000', 'ang', 'operator', 'samtang', 'P18,000', 'ang', 'drayber', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,724 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'video', ',', 'siya', 'nidugang', ',', 'makita', 'nga', 'sobra', 'ang', 'gipangayo', 'nga', 'plite', 'sa', 'drayber', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,725 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'Almalyn', 'Salimbagat', 'Pancho', 'ang', 'ni-post', 'sa', 'iyang', 'Facebook', 'page', 'niadtong', 'Nobiyembre', '29', 'sa', 'alas', '10:11', 'sa', 'buntag', 'ug', 'nipasangil', 'sa', 'taxi', 'drayber', 'nga', 'naningil', 'og', 'P2,000', 'nga', 'plite', 'sa', '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, 1, 2, 2, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,726 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nitug-an', 'ang', 'usa', 'sa', 'mga', 'Hapon', 'ang', 'nikuha', 'sa', 'video', 'ug', 'nipadala', 'niya', 'isip', 'ilang', 'tour', 'guide', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,727 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'ni', 'Mandaue', 'City', 'Mayor', 'Luigi', 'Quisumbing', 'nga', 'mas', 'maayong', 'ibalik', 'sa', 'Philippine', 'National', 'Police', '(', 'PNP', ')', 'sa', 'labing', 'daling', 'panahon', 'ang', 'operasyon', 'batok', 'drugas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,728 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'sa', 'mayor', 'nga', 'kon', 'basehan', 'ang', 'gidaghanon', 'ang', 'mga', 'PDEA', 'agents', ',', 'lisod', 'isustiner', 'ang', 'mga', 'operasyon', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,729 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paambit', 'niini', 'nga', 'maglisod', 'ang', 'mga', 'taga', 'PDEA', 'ilabi', 'na', 'kon', 'ang', 'mga', 'operasyon', 'sa', 'nagkadaiyang', 'adlaw', 'mahitabo', 'sa', 'lagyong', 'rehiyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
6,730 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Saysay', 'niya', 'nga', 'bisan', 'pa', 'man', 'sa', 'massive', 'hiring', 'sa', 'PDEA', 'apan', 'di', 'gyud', 'makaya', 'niini', 'ang', '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, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,731 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'mamahimo', 'kining', 'posible', ',', 'gawas', 'sa', 'mao', 'kini', 'ang', 'labing', 'una', 'sa', 'Central', 'Visayas', ',', 'posible', 'usab', 'kini', 'nga', 'usa', 'ka', 'tourist', 'attraction', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,732 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'sa', 'tigpamaba', 'sa', 'Department', 'of', 'Energy', '(', 'DOE', ')', '-', 'Visayas', 'nga', 'si', 'Lourdes', 'Arciaga', 'nga', 'na-award', 'na', 'sa', 'duha', 'ka', 'kompaniya', 'ang', 'duha', 'ka', 'service', 'contracts', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,733 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Susama', 'sa', 'Bangui', 'Wind', 'Fam', 'sa', 'Ilocos', 'Norte', 'nga', 'gawas', 'sa', 'naghatag', 'kini', 'og', 'supply', 'sa', 'kuryente', ',', 'usa', 'usab', 'kini', 'sa', 'gibisita', 'sa', 'turista', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,734 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Arciaga', 'nga', 'posibleng', 'susama', 'usab', 'kini', 'sa', 'Sugbo', 'apan', 'sa', 'bukid', 'lang', 'nga', 'parte', 'diin', 'anaa', 'sa', 'bahin', 'sa', 'Trans-axial', 'highway', '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, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0] | cebuaner |
6,735 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'duha', 'gilauman', 'nga', 'mohatag', 'og', '741', 'megawatts', 'nga', 'supply', 'sa', 'kuryente', 'pinaagi', 'sa', 'hangin', 'nga', 'dili', 'makadaut', 'sa', 'kalikupan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,736 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kahamhanan', 'sa', 'dakbayan', 'sa', 'Talisay', 'mopatuman', 'nag', 'oras', 'niadtong', 'delivery', 'trucks', 'nga', 'mo-deliver', 'sa', 'ilang', 'produkto', 'sa', 'merkado', 'sa', 'Tabunok', 'ug', 'uban', 'pang', 'dagkong', 'karsada', 'siyudad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,737 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'maoy', 'lakang', 'sa', 'siyudad', 'sa', 'tinguha', 'nga', 'mominusan', 'ang', 'kahuot', 'sa', 'Tabunok', 'sa', 'mga', 'sakyanan', 'ilabi', 'na', 'sa', 'mga', 'oras', 'nga', 'daghan', 'ang', '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, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,738 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'delivery', 'truck', 'maka-deliver', 'sa', 'ilang', 'produkto', 'sa', 'wala', 'pay', 'alas', '6:00', 'sa', 'buntag', 'human', 'sa', 'alas', '10:00', 'sa', 'buntag', 'hangtod', 'na', 'sa', 'ala', '1:00', 'sa', 'hapon', 'ug', 'human', 'na', 'sa', 'alas', '8:00', '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, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
6,739 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'spot', 'report', 'sa', 'Police', 'Regional', 'Office', '8', ',', 'usa', 'ka', 'alarma', 'ang', 'nadawat', 'sa', 'Baybay', 'City', 'Police', 'bahin', 'sa', 'usa', 'ka', 'hubog', 'kaayo', 'nga', 'lalake', 'nga', 'nanghasi', 'sud', 'sa', 'usa', 'ka', 'restaurant', 'sa', 'Rizal', 'Boulevard', ',', 'Zone', '10', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 3, 4, 4, 4, 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, 5, 6, 6, 6, 6, 0] | cebuaner |
6,740 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'SPO1', 'Dondon', 'Labador', 'ug', 'PO1', 'Reynard', 'Morquianos', 'ang', 'miresponde', 'sa', 'alarma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0] | cebuaner |
6,741 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Napahunong', 'nila', 'ang', 'panghasi', 'sa', 'suspek', 'nga', 'si', 'Ramil', 'Luchavez', ',', 'taga', 'Barangay', 'Hilapnitan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 5, 6, 0] | cebuaner |
6,742 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'dihang', 'padung', 'na', 'posasan', ',', 'misuway', 'pag', 'labnot', 'ang', 'suspek', 'ug', 'misuway', 'pag-ilog', 'sa', 'armas', 'ni', 'Labador', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,743 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Capt.', 'Jose', 'Galope', ',', 'spokesperson', 'sa', '802', 'Infantry', 'Brigade', ',', 'nakadawat', 'og', 'report', 'ang', '78th', 'IB', 'nga', 'dunay', 'mga', 'armado', 'ang', 'nakit-an', '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, 1, 2, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,744 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingong', 'nag-extort', 'ang', 'grupo', 'sa', 'mga', 'residente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,745 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pagresponde', 'sa', 'militar', ',', 'naabtan', 'nila', 'ang', 'kapin', 'kun', 'kulang', '10', 'ka', 'CTE', '/', 'NPA', 'sa', 'lugar', 'hinungdan', 'sa', '15', 'minutos', 'nga', 'pinusilay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,746 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Way', 'samdan', 'ug', 'nakalas', 'sa', 'militar', 'samtang', 'wala', 'pa', 'mahibaw-i', 'sa', 'pikas', 'grupo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,747 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'tulo', 'dunay', 'warrant', 'of', 'arrest', 'sa', 'kasong', 'Frustrated', 'Murder.', 'Nakadawat', 'og', 'intelligence', 'report', 'ang', 'kapulisan', 'sa', 'Allen', 'nga', 'toa', 'sa', 'Cavite', 'ang', 'ilang', 'gipangita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0] | cebuaner |
6,748 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasikop', 'ang', 'tulo', 'kinsa', 'adunay', 'tag', 'P200,000', 'nga', 'piyansa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,749 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'usab', 'nga', 'si', 'Tampos', 'napriso', 'tungod', 'sa', 'kaso', 'sa', 'gidiling', 'drugas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0] | cebuaner |
6,750 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Fuego', 'nga', 'sa', 'dihang', 'girikisa', 'niya', 'ang', 'mga', 'butang', 'nga', 'dala', 'sa', 'suspek', 'nga', 'mobisita', 'unta', 'sa', 'nahisgutang', 'piniriso', ',', 'giingon', 'nga', 'narekober', 'niya', 'ang', 'usa', 'ka', 'gamayng', 'putos', 'nga', 'adunay', 'timbang', 'nga', 'lima', 'ka', 'gramos', 'nga', 'gituhoang', 'drugas', 'nga', 'giipon', 'pagsud', 'sa', 'sabon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,751 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipakita', 'ni', 'Supt.', 'Reynaldo', 'Magdaluyo', 'ang', 'CCTV', 'camera', 'footage', 'sa', '7', 'Eleven', 'convenience', 'store', 'sa', 'Barangay', 'Tayud', 'niadtong', 'gabii', 'sa', 'dihang', 'gi', 'ambush', 'patay', 'si', '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, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,752 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Alas', '7:56', 'makita', 'ang', 'pag', 'abot', 'ni', 'Largo', 'nga', 'nisulod', 'sa', '7', 'Eleven', 'ug', 'nipalit', 'og', 'ice', 'cream', 'samtang', 'dunay', 'gipaabot', 'nga', 'iyang', 'kauban', 'nga', 'ilang', 'gituohan', 'nga', 'usa', 'sa', 'mga', 'gun', 'man', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,753 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nag', 'sul-ob', 'og', 'kawo', 'si', 'Largo', 'samtang', 'ang', 'iyang', 'dughan', 'dunay', 'gisab-it', 'nga', 'antipara', 'nga', 'nakuha', 'usab', 'ni', 'sa', 'mga', 'sakop', 'sa', 'Regional', 'Special', 'Operations', 'Group', 'atol', 'sa', 'gihimong', 'manhunt', 'operation', 'kaniya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,754 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'pa', 'siyay', 'nasugatan', 'nga', 'kaila', 'nga', 'duna', 'usay', 'gipalit', 'sa', 'tindahan', 'ug', 'pipila', 'ka', 'gutlo', 'ang', 'nakalabay', ',', 'nigawas', 'ni', 'sa', 'tindahan', 'ug', 'daw', 'sa', 'dunay', 'gipaabot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,755 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Positibo', 'usab', 'ning', 'giila', 'sa', 'tulo', 'ka', 'mga', 'saksi', 'nga', 'ang', 'naa', 'sa', 'video', 'sa', 'CCTV', 'camera', 'mao', 'si', 'Largo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,756 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pagka', 'alas', '8:29', ',', 'makita', 'sa', 'video', 'nga', 'niagi', 'ang', 'Isuzu', 'D-Max', 'ni', 'Rupinta', 'nga', 'dunay', 'nag-una', 'nga', 'duha', 'ka', 'mga', 'motorsiklo', 'nga', 'gituohan', 'nga', 'apil', '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, 0, 0, 7, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,757 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nisunod', 'usab', 'dayon', 'silang', 'Largo', 'ug', 'ang', 'kauban', '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, 1, 0, 0, 0, 0, 0] | cebuaner |
6,758 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'dihang', 'pag', 'abot', 'sa', 'eskina', ',', 'ang', 'duha', 'ka', 'mga', 'motorsiklo', 'nga', 'nag-una', 'sa', 'sakyanan', 'sa', 'kapitan', 'niliko', 'sa', 'tuo', 'samtang', 'silang', 'Largo', 'mi', 'overtake', 'sa', 'sakyanan', 'ug', 'sa', 'dihang', 'natungdan', 'na', 'gipaarakan', 'na', 'kini', 'sa', 'armas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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] | cebuaner |
6,759 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'pagpamusil', ',', 'nilili', 'pa', 'si', 'Largo', 'aron', 'pagsiguro', 'nga', 'patay', 'ang', 'ilang', 'target', 'ug', 'misinggit', 'pa', 'kini', 'nga', 'kompirmadong', 'patay', 'na', 'ang', '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, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,760 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'ni', 'Magdaluyo', 'nga', 'ang', 'kuha', 'sa', 'CCTV', 'mitugma', 'sa', 'pamahayag', 'ni', 'Jocilyn', 'Mendoza', 'nga', 'nagkawo', 'lang', 'si', 'Largo', 'ug', 'wala', 'mag', 'helmet', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] | cebuaner |
6,761 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nianang', 'adlawan', 'pa', 'mga', 'pasado', 'alas', '2', 'sa', 'hapon', ',', 'nakit-an', 'na', 'sa', 'tulo', 'ka', 'mga', 'saksi', 'si', 'Largo', 'kauban', 'si', 'alyas', 'Jordan', 'nga', 'nagduwa', 'og', 'bingo', 'sa', 'atbang', 'sa', 'barangay', 'hall', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,762 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pasado', 'alas', '6', 'sa', 'gabii', 'sa', 'dihang', 'nikanaog', 'si', 'Rupinta', 'uban', 'ni', 'Jocilyn', 'ug', 'nisakay', 'sa', '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, 1, 0, 0, 1, 0, 0, 0, 0, 0] | cebuaner |
6,763 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dali', 'usab', 'nga', 'miangkas', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'motor', 'silang', 'Largo', 'ug', 'alyas', 'Jordan', 'ug', 'nananghid', 'pa', 'nga', 'ibilin', 'lang', 'ang', 'ilang', 'pundo', 'nga', 'kwarta', 'sa', 'bingo', 'kay', 'ila', 'lang', 'kining', 'balikon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,764 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'facial', 'composite', 'ni', 'Jordan', 'gipakita', 'sa', 'media', 'nga', 'gihulagway', 'ni', 'Magdaluyo', 'nga', 'hired', 'killer', 'sa', 'Barangay', 'Ermita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 1, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
6,765 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niining', 'maong', 'kalamboan', 'gipanghimakak', 'ni', 'Magdaluyo', 'ang', 'pangangkon', 'ni', 'Largo', 'nga', 'dili', 'siya', 'ang', 'nagpatay', 'sa', 'kapitan', 'ug', 'fall', 'guy', 'lamang', 'siya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,766 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nihatag', 'na', 'usab', 'og', 'pamahayag', 'ang', 'igsuon', 'ni', 'kapitan', 'Rupinta', 'nga', 'dili', 'tinuod', 'nga', 'na-', 'uban', 'sila', 'ni', 'Largo', 'nianang', 'gabii', 'nga', 'gipatay', 'ang', '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, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,767 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lima', 'ngadto', 'sa', 'unom', 'ka', 'mga', 'suspek', 'ang', 'giingong', 'nagtinabangay', 'aron', 'patyon', 'ang', 'kapitan', 'ug', 'wala', 'lang', 'una', 'moluwat', 'og', 'pamahayag', 'ang', 'RSOG7', 'kon', 'unsay', 'motibo', 'sa', 'pagkakaron', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 0, 0, 0, 0, 0, 0] | cebuaner |
6,768 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duol', 'na', 'lang', 'nga', 'mahimong', 'beato', 'si', 'anhing', 'Archbishop', 'Teofilo', 'Camomot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,769 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Fr.', 'Mhar', 'Vincent', 'Balili', ',', 'vice', 'postulator', 'sa', 'cause', 'ni', 'Camomot', ',', 'nikompirmar', 'nga', 'nakadawat', 'silag', 'suwat', 'gikan', 'kang', 'Cardinal', 'Angelo', 'Amato', ',', 'prefect', 'sa', 'Congregation', ',', 'sa', 'ilang', 'hukom', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,770 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'suwat', ',', 'si', 'Amato', 'niingon', 'nga', 'nakadawat', 'sila', 'og', 'duha', 'ka', 'diocesan', 'inquiries', 'sa', 'kinabuhi', 'ni', 'Camomot', ',', 'nga', 'pulos', 'giduso', 'sa', 'Archdiocese', 'sa', '2015', 'ug', '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, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,771 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pasabot', 'niini', 'nga', 'kon', 'anaa', 'ka', 'sa', 'maong', 'dapit', ',', 'kinahanglan', 'ka', 'nga', 'motuman', 'sa', 'tanang', 'mga', 'lagda', 'sa', 'balaud', 'gikan', 'sa', 'basura', ',', 'trapiko', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,772 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'tinguha', 'nga', 'hingpit', 'na', 'mawagtang', 'ang', 'nagkadaiyang', 'mga', 'krimen', 'ug', 'magpuyo', 'ang', 'katawhan', 'sa', 'Central', 'Visayas', 'ug', 'malinawon', 'ug', 'hapsay', 'nga', 'komunidad', ',', 'hinungdan', 'nga', 'ilusad', 'ang', 'discipline', 'zone', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,773 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'pa', 'ni', 'Villaro', 'nga', 'tagan-an', 'og', 'upat', 'ka', 'buwan', 'ang', 'pagpatuman', 'sa', 'maong', 'programa', 'diin', 'matag', 'semana', 'adunay', 'ipahigayong', 'evaluation', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,774 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'maayo', 'ang', 'makitang', 'resulta', ',', 'hayan', 'palapdan', 'kini', 'hangtud', 'nga', 'malukop', 'ang', 'tibuok', 'siyudad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,775 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Margot', 'nga', 'iya', 'kining', 'gihimo', 'nga', 'lakang', 'tungod', 'kay', 'nakita', 'niyai', 'nga', 'nagkinahanglan', 'ang', 'Ormoc', 'sa', 'maong', 'financial', 'assistance', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0] | cebuaner |
6,776 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Fernandez', 'nga', 'kon', 'moabot', 'na', 'ang', 'MOA', ',', 'pirmahan', 'kini', 'sa', 'mayor', 'ug', 'i-deposito', 'dayon', ',', 'matod', 'pa', ',', 'ang', 'kwarta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,777 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'sa', 'mga', 'nagpuyo', 'daplin', 'sa', 'dagat', 'sa', 'Sityo', 'Sperla', ',', 'Barangay', 'Poblacion', ',', 'Lapu-Lapu', 'City', 'ang', 'demolison', 'karong', 'adlawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
6,778 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Balay', 'nga', 'unahon', 'sa', 'pagdemolish', 'mao', 'kadtong', 'nagbarog', 'sa', 'dagat', 'ug', 'kadtong', 'struktura', 'nga', 'gipa-abangan', 'sa', 'professional', 'squatters', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,779 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'human', 'niadtong', 'tulo', 'ka', 'balay', 'nga', 'nalusno', 'sa', 'dihang', 'gihampak', 'sa', 'bawod', 'resulta', 'sa', 'naghaguros', 'nga', 'fastcraft', 'nga', 'mitadlas', 'sa', 'Mactan', 'Channel', 'niadtong', 'buwan', 'sa', 'Oktubre', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 6, 0, 0, 0, 0, 0] | cebuaner |
6,780 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Radaza', 'sa', 'iyang', 'bahin', 'nipasabot', 'nga', 'na-ungaw', 'sa', 'peligro', 'ang', 'kahimtang', 'sa', 'ubang', 'lumolupyo', 'hinungdan', 'nga', 'buot', 'niyang', 'papahawaon', 'ug', 'mobalhin', 'sa', 'luwas', 'nga', '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, 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 |
6,781 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'buot', 'ang', 'mayor', 'nga', 'masubli', 'ang', 'panghitabo', 'sa', 'dihang', 'duha', 'ka', 'mga', 'senior', 'citizen', 'ang', 'naangol', 'nga', 'nangahulog', 'sa', 'dagat', 'ug', 'gi-rescue', 'sa', 'mga', 'silingan', 'sa', 'dihang', 'balay', 'natumpag', 'sa', 'bawod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,782 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'gi-schedule', 'ang', 'demolition', 'niadtong', 'Nobiyembre', '8', 'apan', 'mga', 'sakop', 'sa', 'Presidential', 'Commission', 'for', 'the', 'Urban', 'Poor', '(', 'PCUP', ')', 'nihangyo', 'nga', 'lugwayan', 'ang', 'maong', 'petsa', 'hinungdan', 'nga', 'na-uswag', 'karong', 'adlawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,783 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'laing', 'suwat-hangyo', 'sa', 'PCUP', 'ang', 'gipadala', 'niadtong', 'miaging', 'adlaw', 'ngadto', 'sa', 'buhatan', 'sa', 'mayor', 'nga', 'nangayo', 'og', 'laing', 'extension', 'sa', 'maong', 'demolition', 'gumikan', 'sa', 'humanitarian', 'reason', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0] | cebuaner |
6,784 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'pa', 'sa', 'mayor', 'nga', 'wa', 'magpasagad', 'ang', 'siyudad', 'sanglit', 'hatagan', 'man', 'ang', 'mga', 'apektadong', 'nga', 'structure', 'owner', 'og', 'financial', 'aid', 'ug', 'relief', 'goods', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,785 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pawikan', 'dunay', 'samad', 'sa', 'iyang', 'liog', 'agi', 'sa', 'pana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,786 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nihangyo', 'sa', 'mga', 'opisyal', 'nga', 'imbestigahon', 'ang', 'illegal', 'nga', 'panagat', 'sa', 'lungsod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,787 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Cirilo', 'Tapales', ',', 'kapitan', 'sa', 'Barangay', 'Basdiot', ',', 'nisulti', 'sa', 'SunStar', 'Cebu', 'nga', 'natugyan', 'na', 'ngadto', 'sa', 'kapulisan', 'sa', 'ilang', 'lungsod', 'ang', 'pawikan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 5, 6, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,788 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nagtuo', 'ang', 'mga', 'mananagat', 'sa', 'laing', 'barangay', 'ang', 'posible', 'nagpana', 'sa', 'pawikan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,789 | 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', 'illegal', 'ang', 'pagpana', 'kun', 'spear', 'hunting', 'sa', 'Moalboal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
6,790 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mananagat', 'nga', 'masakpan', 'nakalapas', 'sa', 'maong', 'balaod', 'mahimo', 'mapriso', 'hangtod', 'sa', '10', 'ka', 'tuig', 'o', 'pamultahon', 'og', 'P500,000', 'sa', 'matag', 'pawikan', 'nga', 'iyang', 'mapatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,791 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ahmed', 'Cuizon', ',', 'director', 'sa', 'LTFRB', '7', ',', 'nikompirmar', 'nga', 'alas', '10', 'karong', 'buntag', 'mahitabo', 'ang', 'hearing', 'sa', 'taxi', 'drayber', 'nga', 'si', 'Jack', '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, 1, 2, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,792 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'P2,000', 'ang', 'gipaningil', 'nga', 'plite', 'sa', 'drayber', 'apan', 'base', 'sa', 'video', ',', 'P1,500', 'ang', 'gihatag', 'sa', 'langyaw', 'nga', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,793 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'staff', 'sa', 'Gilgal', 'Taxi', 'nibutyag', 'nga', 'wala', 'mamasahero', 'si', 'Espanto', 'kagahapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 1, 0, 0] | cebuaner |
6,794 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'maong', 'staff', ',', 'nag-text', 'ang', 'drayber', 'nga', 'dili', 'maka-drive', 'kagahapon', 'apan', 'napahibawo', 'na', 'nila', 'sa', 'pagpatawag', 'sa', 'LTFRB', '7', '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, 3, 4, 0, 0] | cebuaner |
6,795 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nibanabana', 'nga', '40', 'porsiyento', 'lang', 'sa', '600,000', 'ka', 'mga', 'bata', 'nga', 'nagpanuigon', 'og', '9-anyos', 'hangtod', '14-anyos', 'nga', 'target', 'populasyon', 'nga', 'hatagan', ',', 'ang', 'nabakunahan', 'sa', 'unang', 'hugna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,796 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Blanco', 'nga', 'hayan', 'mabalibaran', 'ang', 'mga', 'bata', 'nga', 'wala', 'mahatagi', 'og', 'bakuna', 'sa', 'unang', 'hugna', ',', 'apan', 'kini', 'ila', 'pang', 'tukion', 'atol', 'sa', 'ilang', 'tigom', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
6,797 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'record', 'sa', 'Resu', '7', ',', 'nasayran', 'nga', 'niabot', 'na', 'og', '12,098', 'ang', 'mga', 'kaso', 'sa', 'dengue', 'sa', 'Central', 'Visayas', 'gikan', 'sa', 'Enero', 'hangtod', 'sa', 'Nobiyembre', '25', ',', '2017.', 'Sa', 'maong', 'ihap', ',', '108', 'ang', 'nangamatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,798 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', '52', 'porsiyento', 'nga', 'ubos', 'kon', 'itandi', 'sa', 'samang', 'panahon', 'sa', 'miaging', 'tuig', 'nga', 'niabot', 'og', '25,466', 'ang', 'mga', 'kaso', 'ug', '221', 'ang', 'nangamatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,799 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kasagaran', 'sa', 'mga', 'nagka-dengue', 'kay', 'nagpangidaron', 'og', 'usa', 'ka', 'tuig', 'hangtod', '5-anyos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
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