Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,600 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'suwerteng', 'tawo', 'nagkupot', 'sa', 'winning', 'number', 'combination', '37-35-45-11-24-22.', 'Ang', 'Grand', 'Lotto', 'bolahon', 'sa', 'Lunes', ',', 'Miyerkules', 'ug', 'Sabado', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,601 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'ang', 'mga', 'opisyal', 'sa', 'siyudad', 'nagplano', 'sa', 'paggamit', 'sa', 'pipila', 'ka', 'bahin', 'sa', 'South', 'Road', 'Properties', '(', 'SRP', ')', 'isip', 'alternationg', 'dapit', 'sa', 'mga', 'party', 'diin', 'pwede', 'mabaligya', 'ang', 'mga', 'ilimnong', 'makahubog', 'atol', 'Sinulog', 'festivities', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0] | cebuaner |
6,602 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Cebu', 'City', 'Police', 'Office', '(', 'CCPO', ')', 'nagkanayon', 'nga', 'makatutok', 'pag-ayo', 'sa', 'pagbantay', 'sa', 'seguridad', 'kon', 'ang', 'lingawlingaw', 'naa', 'sa', 'usa', 'ka', 'lugar', 'lang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,603 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', ',', 'ang', 'business', 'establishments', 'sud', 'sa', '300', 'meters', 'sa', 'parade', 'route', ',', 'labina', 'ang', 'bar', 'owners', 'niingon', 'nga', 'kini', 'makakibhang', 'og', 'dako', 'sa', 'ilang', 'kita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,604 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'public', 'hearing', ',', 'usa', 'ka', 'representante', 'sa', 'maapektuhang', 'establisemento', 'nasagmuyo', 'nga', 'did-an', 'nga', 'makabaligya', 'og', 'ilimong', 'makahubog', 'sa', 'Enero', '21', ',', 'busa', 'maayong', 'mahatagan', 'sila', 'og', 'tax', 'relief', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,605 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Parehong', 'patay', 'ang', 'drayber', 'sa', 'duha', 'ka', 'mga', 'motorsiklo', 'human', 'sa', 'ilang', 'panagbangga', 'sa', 'Barangay', 'Don', 'Andres', 'Soriano', ',', 'dakbayan', 'sa', 'Toledo', 'City', 'niadtong', 'Sabado', 'sa', 'gabii', ',', 'diin', 'tulo', 'ang', 'naangol', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 6, 6, 6, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,606 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'duha', 'parehong', 'dunay', 'dakong', 'samad', 'sa', 'ulo', 'ug', 'ubang', 'bahin', 'sa', 'lawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,607 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', ',', 'angol', 'sila', 'si', 'Robert', 'Camanso', ',', '31', ';', 'Robert', 'Mercader', ',', '18', ';', 'ug', 'Marvin', 'Sobeto', ',', '23', ',', 'nga', 'wa', 'nagsul-ob', 'og', '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, 0, 0, 0, 1, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,608 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'motorsiklo', 'ni', 'Tura', 'nawad-an', 'og', 'kontrol', 'busa', 'nikawat', 'sa', 'pikas', 'lane', 'busa', 'nakasugat', 'ang', 'motorsiklo', 'nga', 'gimaneho', 'ni', 'Gilbert', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 0] | cebuaner |
6,609 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Gilbert', 'ug', 'iyang', 'mga', 'angkas', 'mao', 'sila', 'si', 'Mercader', 'ug', 'Sobeto', 'nangatumba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 1, 0, 0] | cebuaner |
6,610 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sila', 'si', 'Tura', 'ug', 'Camanso', 'nangalabay', 'sa', 'ilang', 'mtorsiklo', ',', 'sumala', 'ni', 'PO2', 'Jonathan', 'Cuizon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,611 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'medical', 'team', 'ang', 'nidala', 'sa', 'mga', 'biktima', 'sa', 'Carmen', 'Copper', 'Hospital', ',', 'diin', 'ang', 'mga', 'doktor', 'nideklarar', 'nila', 'ni', 'Tura', 'ug', 'Gilbert', 'nga', 'dead-on-', 'arrival', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0] | cebuaner |
6,612 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitinguha', 'karon', 'sa', 'kapulisan', 'sa', 'dakbayan', 'sa', 'Talisay', 'nga', 'duha', 'ka', 'barangay', 'ang', 'madeklarar', 'na', 'nga', 'drug', 'cleared', 'sa', 'di', 'pa', 'mahuman', 'ang', 'tuig', '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, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,613 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'matod', 'ni', 'Villamater', 'di', 'siya', 'mosaad', 'apan', 'iyang', 'paninguhaon', 'nga', 'makadeklarar', 'silag', 'drug', 'cleared', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,614 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'giklaro', 'nga', 'kaniadto', 'sa', 'miaging', 'hepe', 'mahimo', 'na', 'untang', 'madeklarar', 'ang', 'Tapul', 'nga', 'drug', 'cleared', 'apan', 'napugngan', 'human', 'nga', 'adunay', 'mi-surrender', 'na', 'apan', 'nasikop', 'og', 'balik', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
6,615 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Villamater', 'nga', 'kining', 'duha', 'ka', 'barangay', 'sa', 'Talisay', 'maoy', 'di', 'kaayo', 'problema', 'sa', 'drugas', 'ug', 'gamay', 'ra', 'usab', 'hinungdan', 'nga', 'di', 'kini', 'lisod', 'nga', 'ipadeklarar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,616 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'wa', 'pa', 'nila', 'paghatag', 'og', 'gahum', 'nga', 'maka-operate', 'og', 'balik', 'usa', 'sa', 'rason', 'nga', 'di', 'kaayo', 'sila', 'makalihuk', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,617 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihangyo', 'ni', 'sab', 'ni', 'Villamater', 'ang', 'mga', 'opisyal', 'sa', 'barangay', 'nga', 'moabag', 'usab', 'kanilang', 'kapulisan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0] | cebuaner |
6,618 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Rupinta', 'gibanhigan', 'sa', 'Tayud', ',', 'lungsod', 'sa', 'Lilo-an', 'sa', 'miaging', 'buwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0] | cebuaner |
6,619 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Kapitan', 'Ramil', 'Ayuman', 'sa', 'Barangay', 'Apas', 'nagkanayon', 'nga', 'di', 'mominos', '50', 'ka', 'mga', 'kapitan', 'ang', 'ninglagda', 'sa', 'manifesto', 'kinsa', 'ningsinggit', 'ug', 'nangayo', 'og', 'hustisya', 'uban', 'sa', 'tinguha', 'nga', 'matumbok', 'ang', 'mastermind', '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, 1, 2, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,620 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Rupinta', 'maoy', 'labing', 'unang', 'kapitan', 'sa', 'barangay', 'kinsa', 'gipatay', 'pinaagi', 'sa', 'pagbanhig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,621 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'opisyal', 'sa', 'barangay', 'nipadangat', 'sa', 'ilang', 'pahasubo', 'ngadto', 'sa', 'pamilya', 'ni', 'Rupinta', 'kinsa', 'nagbangotan', 'sa', 'iyang', 'pagtaliwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
6,622 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasagap', 'nga', 'kasayuran', 'ang', 'kampo', 'ni', 'Rupinta', 'nga', 'dunay', 'mohimo', 'og', 'kaguliyang', 'sa', 'barangay', 'atol', 'sa', 'iyang', 'paglubong', 'karong', 'Disyembre', '8', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,623 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'ilang', ''intelligence', 'report', ''', 'mao', 'ang', 'pagmugna', 'og', 'sunog', 'sa', 'sityo', 'Bato', 'aron', 'lawgawon', 'ang', 'lubong', 'ni', 'Rupinta', 'aron', 'gamay', 'lang', 'ang', 'mohatod', 'sa', 'iyang', 'lubong', 'sa', 'Queen', 'City', 'Memorial', 'Garden', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 6, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0] | cebuaner |
6,624 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'wala', 'kini', 'makompirmar', 'sa', 'awtoridad', 'nga', 'taho', 'apan', 'ila', 'nang', 'gialerto', 'ang', 'Bureau', 'of', 'Fire', 'Protection', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0] | cebuaner |
6,625 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugod', 'ugmang', 'adlawa', 'Lunes', ',', 'hingpit', 'na', 'nga', 'ipatuman', 'sa', 'kagamhanan', 'sa', 'dakbayan', 'sa', 'Sugbo', 'ug', 'kapulisan', 'ang', 'discipline', 'zone', 'sa', 'Osmeña', 'Boulevard', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
6,626 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'nagpasabot', 'nga', 'hawanan', 'na', 'ang', 'sidewalk', 'aron', 'mahatagan', 'og', 'agianan', 'ang', 'katawhan', 'nga', 'moagi', 'sa', 'sidewalk', ',', 'di', 'sa', 'dalan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,627 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', ',', 'usa', 'si', 'Konsehal', 'Dave', 'Tumulak', ',', 'chairman', 'on', 'public', 'order', 'and', 'safety', ',', 'sa', 'ninglakaw', 'uban', 'sa', 'kapulisan', 'aron', 'pagsusi', 'sa', 'unsa’y', 'posible', 'nga', 'mahitabo', 'sa', 'pagpatuman', 'na', 'niini', 'ugma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,628 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Tumulak', 'nga', 'gitan-aw', 'nila', 'ang', 'mga', 'nagbabag', 'sa', 'sidewalk', 'hilabi', 'na', 'kining', 'unregulated', 'nga', 'mga', 'manindahay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,629 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', 'nga', 'gusto', 'nila', 'nga', 'mahibalik', 'ang', 'silbi', 'sa', 'sidewalk', 'ug', 'mao', 'kini', 'nga', 'malakwan', 'sa', 'katawhan', 'nga', 'way', 'nagbabag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,630 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'Arce', 'nga', 'nihangyo', 'sila', 'ug', 'nauyunan', 'sa', 'miaging', 'adlaw', 'nga', 'ilang', 'discipline', 'zone', 'lang', 'una', 'phase', 'by', 'phase', 'diin', 'ang', 'phase', '1', 'gikan', 'sa', 'Capitol', 'paingon', 'sa', 'R.', 'Landon', ',', 'kanang', 'eskina', 'duol', 'sa', 'Camp', 'Sergio', 'Osmeña', 'sa', 'Police', 'Regional', 'Office', '(', 'PRO', ')', '7', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 6, 6, 6, 6, 6, 0] | cebuaner |
6,631 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Tumulak', 'nagkanayon', 'nga', 'ang', 'PROBE', 'mao', 'ang', 'moguba', 'sa', 'nag-ali', 'nga', 'mga', 'structure', 'sa', 'sidewalk', ',', 'ang', 'CCTO', 'mao', 'ang', 'mo-clamp', 'kon', 'ilegal', 'nga', 'nagpark', 'sa', 'sidewalk', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,632 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nangatulog', 'sila', 'sa', 'iyang', 'pamilya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0] | cebuaner |
6,633 | 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', 'usa', 'sa', 'mga', 'suspek', 'sa', 'pagpatay', 'kang', 'Ermita', 'Brgy.', 'Captain', 'Felicisimo', '“Imok”', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 1, 2, 2, 0] | cebuaner |
6,634 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Gera', 'nga', 'klarohon', 'unta', 'sa', 'kapuyo', 'ni', 'Rupinta', 'nga', 'si', 'Jocilyn', 'Mendoza', 'ang', 'alegasyon', 'niini', 'batok', 'kaniya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0] | cebuaner |
6,635 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Iya', 'kong', 'gipasanginlan', ',', 'wa', 'gyud', 'ko', 'kahibawo', 'aning', 'panghitaboa.Kuyog', 'ko', 'sa', 'akong', 'pamilya', 'nangatulog', 'sa', 'balay.', 'Dako', 'nalang', 'ang', 'akung', 'kahibulong', 'nga', 'gipasanginlan', 'nga', 'usa', 'ko', 'sa', 'nagpatay.', 'Kaila', 'mi', 'ni', ',', 'Kap', ',', 'diha', 'mi', 'sa', 'barangay', 'nangwarta', ',', 'wala', 'koy', 'ikasulti', 'niya', 'kay', 'maayo', 'siya', 'nga', 'tawo', ',', '”', 'matod', 'ni', 'Gera.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] | cebuaner |
6,636 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niangkon', 'siya', 'nga', 'napriso', 'na', 'na', 'siya', 'kaniadto', 'dihang', 'gipasanginlan', 'usab', 'siya', 'niadtong', '2004', 'nga', 'nipatay', 'og', 'tawo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,637 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'nakagawas', 'ra', 'siya', 'dihang', 'gi-withdraw', 'ang', 'kaso', 'kay', 'dili', 'siya', 'mao', 'ang', 'nagpatay', '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] | cebuaner |
6,638 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'usab', 'siya', 'masuko', 'nga', 'gipahawanan', 'sa', 'barangay', 'ang', 'lugar', 'diin', 'siya', 'naninda', 'og', 'mga', 'lamas', 'sanglit', 'wa', 'siya', 'maapektohi', 'kay', 'naa', 'ra', 'gihapon', 'sila', 'sa', 'iyang', 'pwesto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,639 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'pagbanhig', 'patay', 'kang', 'Rupinta', ',', 'naa', 'ra', 'siya', 'sa', 'ilang', 'balay', 'gihilot', 'sa', 'iyang', 'anak', 'nga', 'nagpangidaron', 'og', '18', 'anyos', 'ug', 'kini', 'ang', 'makapamatuod', 'nga', 'inosente', 'siya', 'sa', 'pasangil', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,640 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'siyay', 'daghang', 'saksi', 'nga', 'makapamatuod', 'nga', 'naa', 'ra', 'siya', 'sa', 'ilang', 'balay', 'uban', 'sa', 'iyang', 'pamilya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,641 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giangkon', 'ni', 'Gera', 'nga', 'nag', 'bingo', 'siya', 'uban', 'ni', 'Jimmy', 'Largo', 'usa', 'ka', 'adlaw', 'human', 'gipusil', 'si', 'Rupinta', 'sukwahi', 'sa', 'pangangkon', 'sa', 'tulo', 'ka', 'mga', 'saksi', 'nga', 'Huwebes', 'sa', 'alas', '2', 'sa', 'hapon', 'hangtod', 'sa', 'alas', '6', '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, 1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,642 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'usab', 'siya', 'maninda', 'sa', 'mao', 'nga', 'adlaw', 'kay', 'naglain', 'ang', 'iyang', 'lawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,643 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'Police', 'Regional', 'Office', '7', '(', 'PRO', ')', 'hugot', 'nga', 'nibarog', 'nga', 'si', 'Gera', 'lakip', 'sa', 'nipatay', 'kang', 'kapitan', 'Rupinta', 'ug', 'pulos', 'lang', 'alibi', 'ang', 'depensa', '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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,644 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Sr.', 'Supt.', 'Jonathan', 'Cabal', 'nga', 'dunay', 'positibong', 'pag', 'ila', 'kang', 'Gera', 'nga', 'naa', 'siya', 'sa', 'crime', 'scene', 'sa', 'dihang', 'gihimo', 'ang', 'pagpamanhig', 'sa', '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, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,645 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'Lunes', 'isang-at', 'na', 'sa', 'PRO', '7', 'ang', 'kaso', 'batok', 'ni', 'Gera', 'ug', 'gitinguha', 'usab', 'nila', 'nga', 'masikop', 'ang', 'laing', 'duha', 'nga', 'pulos', 'mga', 'sakop', 'sa', 'gun', 'for', 'hire', 'nga', 'naglihok', '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, 3, 4, 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, 5, 0] | cebuaner |
6,646 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'hinabang', 'alang', 'sa', 'mga', 'biktima', 'sa', 'linog', 'sa', 'Ormoc', 'apan', ',', 'wala', 'dayon', 'mahatag', 'tungod', 'sa', 'pipila', 'ka', 'kontrobersya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,647 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giklaro', 'sa', 'mayor', 'nga', 'nangita', 'pa', 'sila', 'og', 'pundo', 'alang', 'sa', 'Ormoc', 'tungod', 'sa', 'pag-priority', 'sa', 'pagtabang', 'sa', 'Marawi', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
6,648 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Osmena', ',', 'dunay', 'P1', 'milyones', 'nga', 'gitagana', 'ang', 'Probinsiya', 'sa', 'Sugbo', 'apan', 'hangtod', 'karon', 'wala', 'gihapon', 'makolekta', 'tungod', 'sa', 'susamang', 'problema', ',', 'dili', 'interesado', 'ang', 'mayor', 'sa', 'Ormoc', 'ug', 'hangtod', 'karon', 'wa', 'gihapon', 'kini', 'balos', 'sa', 'ilang', 'tanyag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 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] | cebuaner |
6,649 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'gusto', 'si', 'Osmeña', 'nga', 'silotan', 'ang', 'mga', 'tawo', 'sa', 'Ormoc', 'ug', 'mao', 'ni', 'ang', 'iyang', 'rason', 'nganong', 'ipadayon', 'ang', 'paghatag', 'og', 'hinabang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,650 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Osmena', ',', 'walay', 'respeto', 'si', 'Mayor', 'Richard', 'Gomez', 'sa', 'iyang', 'pamaagi', 'sa', 'pagdala', 'sa', 'iyang', 'tahas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,651 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'kwarta', 'kay', 'gikan', 'sa', 'mga', 'tawo', 'sa', 'Cebu', 'ug', 'daghan', 'pang', 'pwede', 'magamit', 'ani', 'sa', 'lain', 'pa', 'nga', 'butang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,652 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'aksidenteng', 'pagbuto', 'sa', 'baligyang', 'mga', 'pabuto', 'sa', 'Barangay', 'Babag', ',', 'dakbayan', 'sa', 'Lapu-Lapu', 'diin', 'unom', 'ka', 'tawo', 'ang', 'angol', ',', 'ang', 'mga', 'namaligyaay', 'og', 'pabuto', 'gitigom', 'sa', 'awtoridad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,653 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'dealer', 'ug', 'tighimo', 'sa', 'mga', 'pabuto', 'ug', 'fireworks', 'sa', 'dakbayan', 'gimanduan', 'nga', 'mobaligya', 'lang', 'sa', 'mga', 'produkto', 'nga', 'gitrugutan', 'sa', 'otoridad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,654 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'panagtagbo', ',', 'giingnan', 'ni', 'Kapitan', 'Epifania', 'Augusto', 'ang', 'mga', 'dealer', 'ug', 'tighimo', 'nga', 'bag-ong', 'balaod', 'ang', 'ipatuman', 'base', 'sa', 'mando', 'ni', 'Mayor', 'Paz', 'Radaza', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,655 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['May', 'bag-ong', 'listahan', 'sab', 'nga', 'gihatag', 'ang', 'kagamhanan', 'sa', 'dakbayan', 'sa', 'mga', 'klase', 'sa', 'pabutong', 'mahimong', 'ibaligya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,656 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Sinsp.', 'Felix', 'Cleopas', 'III', ',', 'hepe', 'sa', 'Station', '3', 'nagkanayon', 'nga', 'di', 'mahimong', 'makapamaligya', 'ang', 'mga', 'tindera', 'kon', 'di', 'hingpit', 'makakuha', 'og', 'mga', 'permiso', 'ug', 'clearance', 'sa', 'barangay', ',', 'pulis', ',', 'ug', 'city', '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, 1, 2, 2, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,657 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niini', ',', 'ang', 'mga', 'paboto', 'o', 'pyrotechnics', 'nga', 'ipamaligya', 'nga', 'wa', 'nalakip', 'sa', 'listahan', 'imbarguhon', 'sa', 'ilang', 'buhatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,658 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'haya', 'nga', 'ilang', 'gibilaran', 'niresulta', 'kini', 'sa', 'kamatayon', 'sa', 'usa', 'ka', 'amahan', 'niadtong', 'Biyernes', 'sa', 'hapon', 'sa', 'Sitio', 'Kanlaguno', ',', 'Poblacion', ',', 'lungsod', 'sa', 'Aloguinsan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 6, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
6,659 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'ang', 'amahan', 'nga', 'si', 'Relyboy', 'Biton', ',', '33', ',', 'samtang', 'angol', 'usab', 'iyang', 'asawa', 'nga', 'si', 'Remalie', ',', '29', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
6,660 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'PO2', 'Marvin', 'Hubahib', 'nga', 'kuyog', 'ilang', 'anak', 'nga', '2-anyos', 'kinsa', 'nagduwa', 'luyo', 'sa', 'lungon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,661 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dihang', 'natang-tang', 'ang', 'kurtina', ',', 'gitarong', 'kini', 'ni', 'Remalie', 'ug', 'dihang', 'nasaghiran', 'niini', 'ang', 'vigil', 'light', 'nga', 'giingong', 'grounded', ',', 'didto', 'kini', 'nakuryentehan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,662 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakabantay', 'si', 'Relyboy', 'nga', 'gitabang', 'iyang', 'asawa', 'ug', 'didto', 'niya', 'kini', 'gibunlot', ',', 'nakuryentehan', 'pud', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,663 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidala', 'sa', 'tambalanan', 'ang', 'managtiayon', 'apan', 'dead-on-arrival', 'si', 'Relyboy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
6,664 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Hubahib', 'nga', 'nakahibaw', 'na', 'ang', 'punerarya', 'sa', 'maong', 'panghitabo', 'ug', 'willing', 'makig-settle', 'ang', 'paryente', 'sa', 'namatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,665 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lawas', 'sa', 'usa', 'ka', 'lalake', 'nakit-ang', 'naglutaw', 'sa', 'kadagatan', 'sa', 'Barangay', 'Pusok', ',', 'dakbayan', 'sa', 'Lapu-Lapu', 'niadtong', 'Biyernes', 'sa', 'buntag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 0, 0, 0, 0, 0] | cebuaner |
6,666 | 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', 'buntag', 'sa', 'dihang', 'nakadawat', 'og', 'tawag', 'ang', 'taga', 'Station', '5', 'ug', 'gipahibalo', 'kini', 'kalabot', 'sa', 'tawo', 'nga', 'naglutaw', 'sa', 'dagat', 'ug', 'wa', 'nay', 'kinabuhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,667 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dali', 'niresponde', 'ang', 'kapulisan', 'ug', 'nihimo', 'og', 'retrieval', '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, 0, 0, 0, 0, 0, 0] | cebuaner |
6,668 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'Atty.', 'Rex', 'Fernandez', ',', 'legal', 'counsel', 'ug', 'tigpamaba', 'sa', 'pamilya', 'Ouano', ',', 'niingon', 'nga', 'wa', 'gikonsulta', 'sa', 'kaparian', 'ang', 'iyang', 'kliyente', 'sa', 'usa', 'nihimo', 'sa', 'dakong', 'kausaban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,669 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nanghimakak', 'ang', 'kaparian', 'niini', 'nga', 'Augustinian', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,670 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasuta', 'ning', 'mantalaan', 'nga', 'sa', 'bag-ong', 'kausaban', 'sa', 'Traslacion', 'sunod', 'tuig', ',', 'modalikyat', 'na', 'ang', 'rota', 'paingon', 'sa', 'dakbayan', 'sa', 'Lapu-', 'Lapu', 'ug', 'didto', 'na', 'kini', 'manukad', 'alang', 'sa', 'fluvial', 'procession', 'pagkaugma', 'sukwahi', 'sa', 'naandan', 'niadto', 'nga', 'magsugod', 'sa', 'Ouano', 'Wharf', 'ang', 'fluvial', 'procession', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0] | cebuaner |
6,671 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Atty.', 'Fernandez', 'nga', 'nakalitan', 'ang', 'pamilya', 'Ouano', 'sa', 'kalit', 'nga', 'kausaban', 'ug', 'nganong', 'wala', 'sila', 'gikonsulta', 'niini', 'sanglit', 'naandan', 'naman', 'sa', 'pamilya', 'Ouano', 'nga', 'motampo', 'isip', 'ilang', 'naandan', 'ug', 'debosyon', 'sa', 'Balaang', 'Bata', 'sud', 'sa', '37', 'na', 'ka', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,672 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'ni', 'Atty.', 'Fernandez', 'nga', 'niadtong', 'Septiyembre', 'nakadawat', 'og', 'suwat', 'ang', 'pamilya', 'Ouano', 'gikan', 'sa', 'mga', 'kaparian', 'nga', 'di', 'na', 'sa', 'Ouano', 'wharf', 'manukad', 'ang', 'fluvial', 'parade', 'ug', 'di', 'na', 'sab', 'gamiton', 'niini', 'ang', 'barko', 'nga', 'kanunay', 'sakyan', 'sa', 'Sto.', 'Niño', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,673 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'nga', 'base', 'sa', 'official', 'statement', 'sa', 'kaparian', 'nga', 'buot', 'palapdan', 'niini', 'ang', 'rota', 'aron', 'mas', 'daghan', 'ang', 'makasalmot', 'apan', 'gikwestiyon', 'kini', 'sa', 'sa', 'iyang', 'kliyente', 'nganong', 'karon', 'lang', 'kini', 'gihimo', 'human', 'sa', 'katuigan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,674 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Atty.', 'Fernandez', 'nagkanayon', 'nga', '37', 'ka', 'tuig', 'ang', 'nilabay', ',', 'si', 'Ernesto', 'Ouano', 'Sr.', 'lang', 'ang', 'nidawat', 'sa', 'hagit', 'ug', 'nidupa', 'sa', 'pagpasakay', 'sa', 'Sto.', 'Niño', 'atol', 'sa', 'prosisyon', 'niini', 'sa', 'kadagatan', 'ug', 'way', 'laing', 'pamilya', 'ang', 'nidawat', 'sa', 'hagit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 2, 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 |
6,675 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'matod', 'sa', 'Order', 'of', 'Augustinian', 'Recollects', 'nga', 'ilang', 'gipahibawo', 'ang', 'pamilyang', 'Ouano', 'sa', 'kausaban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 4, 0, 0, 0, 0, 0, 1, 0, 0, 0] | cebuaner |
6,676 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Reyes', 'niingon', 'nga', 'nipatawag', 'sila', 'og', 'meeting', 'sa', 'executive', 'committee', 'aron', 'hisgutan', 'ang', 'plano', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,677 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidudahang', 'nagpakamatay', 'ang', 'lalaki', 'sud', 'sa', 'iyang', 'kwarto', 'alas', '3:50', 'sa', 'kaadlawon', 'niadtong', 'Huwebes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,678 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'si', 'Jordan', 'Gera', ',', '39', ',', 'kinsa', 'usa', 'ka', 'vendor', 'sa', 'merkado', 'publiko', 'sa', 'Carbon', ',', 'nanghimakak', 'sa', 'iyang', 'kalambigitan', 'sa', 'pagpatay', 'sa', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,679 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Police', 'Regional', 'Director', 'Jose', 'Mario', 'Espino', 'nga', 'usa', 'si', 'Gera', 'nga', 'giila', 'sa', 'mga', 'saksi', 'nga', 'ning-ambush', 'kang', 'Rupinta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,680 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingong', 'usa', 'siya', 'sa', 'mga', 'namusil', 'kang', 'Rupinta', 'uban', 'sa', 'unang', 'nasikop', 'nga', 'si', 'Jimmy', '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, 1, 0, 0, 0, 0, 0, 0, 1, 2, 0] | cebuaner |
6,681 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingon', 'nga', 'suod', 'silang', 'managhigala', 'ni', 'Largo', 'isip', 'mga', 'manindahay', '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, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
6,682 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitumbok', 'usab', 'siyang', 'usa', 'sa', 'mga', 'hired', 'killer', 'sa', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,683 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Espino', 'nga', 'usa', 'ka', 'informant', 'ang', 'nipahibawo', 'kanila', 'sa', 'nahimutangan', 'ni', 'Gera', 'niadtong', 'Huwebes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0] | cebuaner |
6,684 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugod', 'sa', 'mga', 'alas', '10', 'sa', 'gabii', 'niadtong', 'Huwebes', ',', 'ila', 'na', 'kining', 'gipanid-an', 'aron', 'dakpon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,685 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'sa', 'mga', 'ala', '1', 'pasado', 'sa', 'hapon', ',', 'ilang', 'nasikop', 'si', 'Gera', ',', 'kinsa', 'ilang', 'nakuhaan', 'og', 'usa', 'ka', 'motorsiklong', 'XRM', '(', 'plate', 'number', '8543', 'GT', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,686 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Espino', 'niingon', 'nga', 'duna', 'pa', 'silay', 'laing', 'duha', 'ka', 'mga', 'suspek', 'nga', 'gipangita', 'nga', 'kauban', 'sa', 'pagpatay', 'kang', 'Rupinta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,687 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ila', 'na', 'usab', 'nga', 'nailhan', 'ang', 'labing', 'minos', 'duha', 'ka', 'mga', 'mastermind', 'sa', 'pagpatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,688 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Rupinta', 'gibanhigan', 'niadtong', 'Nobiyembre', '24', 'sa', 'gabii', 'dihang', 'paingon', 'na', 'mopauli', 'sa', 'iyang', 'balay', 'sa', 'lungsod', 'sa', 'Liloan', 'uban', 'sa', 'iyang', 'kapuyo', 'nga', 'si', 'Mendoza', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,689 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nanumpa', 'na', 'ang', 'bag-ong', 'kapitan', 'sa', 'Barangay', 'Ermita', 'walo', 'ka', 'adlaw', 'sa', 'di', 'pa', 'ihatod', 'sa', 'lubnganan', 'si', 'si', 'Kapitan', 'Felicisimo', '“Imok', 'Rupinta”', 'kinsa', 'gi-ambush', 'patay', 'sa', 'lungsod', 'sa', 'Liloan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,690 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Miral', 'di', 'unta', 'moasumir', 'sa', 'katungdanan', 'isip', 'kapitan', 'ug', 'huwaton', 'nga', 'malubong', 'si', 'Rupinta', 'apan', 'gumikan', 'sa', 'panginahanglan', ',', 'nasayo', 'iyang', 'paglingkod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,691 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bise', 'Mayor', 'Edgardo', 'Labella', 'nibutyag', 'nga', 'gumikan', 'sa', 'daghang', 'pirmahanan', 'sa', 'Office', 'of', 'the', 'Barangay', 'Captain', ',', 'kinahanglan', 'na', 'siyang', 'molingkod', 'aron', 'moirog', 'ang', 'transaksyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,692 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'bise', 'mayor', 'kinsa', 'nitambong', 'atol', 'sa', 'pagpanumpa', 'ni', 'Miral', 'uban', 'sa', 'unom', 'ka', 'mga', 'konsehal', 'sa', 'Ermita', ',', 'niawhag', 'sa', 'batan-ong', 'kapitan', 'nga', 'ipadayon', 'ang', 'mga', 'maayong', 'legasiya', 'sa', 'kanhi', 'kapitan', 'sama', 'sa', 'tiunay', 'nga', 'pagserbisyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,693 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mayor', 'Tomas', 'Osmeña', 'nagkanayon', 'nga', 'padayon', 'karon', 'nilang', 'gi-monitor', 'kon', 'si', 'kinsa', 'ang', 'mosunod', 'sa', 'pagkolekta', 'sa', 'tag', 'P10', 'nga', 'voluntary', 'donation', 'sa', 'mga', 'vendor', 'nga', 'iyang', 'gihulagway', 'nga', 'illegal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,694 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'niadtong', 'buhi', 'pa', 'si', 'Rupinta', ',', 'siya', 'niingon', 'nga', 'ang', 'ilang', 'koleksyon', 'gamiton', 'sa', 'pagpalit', 'sa', 'spare', 'parts', 'sa', 'barangay', 'vehicles', 'sama', 'sa', 'garbage', 'truck', ',', 'fire', 'truck', ',', 'ambulance', 'ug', 'uban', 'pa', 'sanglit', 'mao', 'kini', 'ilang', 'madalidali', 'para', 'di', 'maparalisar', 'ang', 'pagtunol', 'sa', 'batakang', 'serbisyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
6,695 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Health', 'Secretary', 'Francisco', 'Duque', 'nipapahunong', 'una', 'sa', 'paghatag', 'sa', 'dengue', 'vaccines', 'hangtod', 'makakuha', 'og', 'rekomendasyon', 'gikan', 'sa', 'World', 'Health', 'Organization', '(', 'WHO', ')', 'aron', 'dunay', 'technical', 'ug', 'scientific', 'nga', 'basehanan', 'ang', 'rekomendasyon', 'nga', 'ilang', 'ipatuman', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,696 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nasubo', 'nga', 'giapudapod', 'gihapon', 'sa', 'DOH', 'ang', 'bakuna', 'bisan', 'kon', 'wala', 'pa', 'matubag', 'ang', 'pipila', 'ka', 'mga', 'issue', 'kalabot', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,697 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nabalaka', 'sa', 'posible', 'mahiagoman', 'nga', 'sakit', 'sa', 'kabataan', 'nga', 'nabakunahan', 'na', 'niini', 'sa', 'unang', 'hugna', 'apan', 'nga', 'wala', 'pa', 'magka-dengue', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,698 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'usa', 'ka', 'pamahayag', ',', 'kini', 'niangkon', 'nga', 'dugang', 'mga', 'kaso', 'sa', 'grabe', 'nga', 'sakit', 'ang', 'mahiagoman', 'sa', 'tawo', 'nga', 'mabakunahan', 'sa', 'maong', 'dengue', 'vaccine', 'kon', 'wala', 'pa', 'kini', 'matapti', 'og', 'dengue', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,699 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Sanofi', 'nidugang', 'nga', 'moawhag', 'kini', 'sa', 'mga', 'awtoridad', 'sa', 'panglawas', 'nga', 'pahibaw-on', 'ang', 'mga', 'doktor', 'ug', 'pasyente', 'sa', 'bag-ong', 'impormasyon', 'sa', 'mga', 'nasod', 'diin', 'ang', 'bakuna', 'gitugot', 'nga', 'ibakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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