Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,800 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'dengue', 'gikan', 'sa', 'lamok', 'nga', 'nagdala', 'og', 'virus', ',', 'hinungdan', 'nga', 'kabahin', 'sa', 'kampanya', 'mao', 'ang', 'pagyabo', 'sa', 'mga', 'tubig', 'nga', 'kapuy-an', 'sa', 'lamok', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,801 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Synchronized', 'sa', 'timer', 'sa', 'dakbayan', 'sa', 'Mandaue', 'ang', 'traffic', 'light', 'system', 'sa', 'Lapu-Lapu', 'diin', 'susama', 'ra', 'usab', 'nga', 'supplier', 'ang', 'gipalitan', 'ug', 'mitaud', 'sa', 'kahimanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,802 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Panghinaot', 'sa', 'siyudad', 'nga', 'pinaagi', 'sa', 'maong', 'modern', 'nga', 'kahimanan', 'kaminusan', 'na', 'ang', 'nagkahuot', 'nga', 'trapiko', 'sa', 'naasoyng', 'mga', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,803 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibanhigan', 'ang', 'usa', 'ka', 'manindaay', 'og', 'isda', 'sa', 'Mahayahay', ',', 'Barangay', 'Napo', ',', 'lungsod', 'sa', 'Carcar', ',', 'alas', '9', 'sa', 'buntag', 'niadtong', 'Martes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,804 | 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', ',', 'ang', 'wala', 'mailhing', 'mamumuno', 'nisibat', 'ug', 'wala', 'matio', 'ang', 'motibog', 'sa', 'krimen.', 'Gisubay', 'pa', 'kini', 'sa', 'kapulisan.', '(', 'Michelle', 'Anne', 'Obor', ',', 'USJR', '-', 'intern', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 2, 2, 2, 0, 3, 0, 0, 0] | cebuaner |
6,805 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipangita', 'sa', 'kapulisan', 'nga', 'gipangulohan', 'sa', 'Talamban', 'police', 'inabagan', 'sa', 'Regional', 'Special', 'Operations', 'Group', '7', 'ang', 'tulo', 'ka', 'mga', 'tawo', ',', 'duha', 'niini', 'mga', 'armado', ',', 'nga', 'maoy', 'ningtulis', 'sa', '60', 'anyos', 'nga', 'ginang', 'nga', 'si', 'Elizabeth', 'Seniedo', ',', 'taga', 'Sunny', 'Hills', ',', 'Barangay', 'Talamban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 5, 6, 6, 6, 6, 0] | cebuaner |
6,806 | 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.', 'Insp.', 'Arieza', 'Otida', ',', 'hepe', 'sa', 'Talamban', 'police', 'station', ',', 'si', 'Seniedo', 'gikan', 'nag', 'withdraw', 'ug', 'kwarta', 'sa', 'usa', 'ka', 'bangko', 'nga', 'nahimutang', 'sa', 'usa', 'ka', 'dakong', 'mall', 'sa', 'maong', 'lugar', 'apan', 'sa', 'iyang', 'pagbalik', 'sa', 'iyang', 'sakyanan', 'nga', 'giparking', 'sa', 'basement', 'kalit', 'lang', 'siya', 'nga', 'giduol', 'sa', 'duha', 'ka', 'armadong', 'lalake', 'ug', 'gitionan', 'ug', 'mimando', 'nga', 'mosulod', 'sa', 'iyang', '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, 1, 2, 0, 0, 0, 3, 4, 4, 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, 0, 0, 0, 0, 0, 0] | cebuaner |
6,807 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'sulod', 'sa', 'iyang', 'Toyota', 'Fortuner', '(', 'AED', '3357', ')', 'color', 'brown', ',', 'gisampungan', 'og', 'panapton', 'ang', 'iyang', 'duha', 'ka', 'mga', 'mata', 'ug', 'gidala', 'sa', 'habagatang', 'bahin', 'sa', 'probinsya', '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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,808 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikuha', 'ang', 'iyang', 'relo', 'nga', 'mobalor', 'og', 'P300,000', ';', 'kwarta', 'nga', 'P15,000', 'ug', 'mga', 'butang', 'niini', 'sama', 'sa', 'cellphone', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,809 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'makuha', 'ang', 'maong', 'mga', 'butang', ',', 'gipakanaog', 'siya', 'sa', 'mingaw', 'nga', 'dapit', 'sa', 'Barangay', 'Kanyuko', ',', 'lungsod', 'sa', 'Dumanjug', ',', 'Sugbo', 'ug', 'gihikot', 'sa', 'punoan', 'sa', 'kahoy', 'ang', 'iyang', 'mga', 'kamot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,810 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'siya', 'magapos', ',', 'nanibat', 'ang', 'mga', 'tulisan', 'sakay', 'sa', 'iyang', 'sakyanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,811 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'gutlo', 'ang', 'nakalabay', ',', 'nakabadbad', 'si', 'Seniedo', 'sa', 'higot', 'ug', 'nisakay', 'siya', 'og', 'bus', 'paingon', 'sa', 'lungsod', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,812 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Didto', 'na', 'siya', 'nidangop', 'sa', 'police', 'station', 'sa', 'lungsod', 'ubos', 'sa', 'paggiya', 'sa', 'konduktor', 'aron', 'itug-an', 'ang', 'nahitabo', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,813 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gituohan', 'sa', 'kapulisan', 'nga', 'organisadong', 'grupo', 'ang', 'naghimo', 'sa', 'krimen', ',', 'matod', 'ni', 'Otida', 'kon', 'basehan', 'ang', 'pamahayag', 'ni', 'Seniedo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 0] | cebuaner |
6,814 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gituohan', 'nga', 'sinultihan', 'sa', 'taga', 'Mindanao', 'ug', 'giingong', 'dunay', 'naghatag', 'og', 'idiya', 'kung', 'asa', 'moagi', 'nga', 'dili', 'madakpan', 'sa', 'mga', 'police', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,815 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'mga', 'tulisan', 'nangayog', 'pasaylo', 'kaniya', 'kay', 'tulis', 'ra', 'ang', 'ilang', 'tuyo', 'kay', 'nagkinahanglan', 'siyag', 'kwarta', 'tungod', 'kay', 'manganakay', 'ang', 'iyang', 'asawa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,816 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'sinugdan', 'giingong', 'duha', 'ra', 'ka', 'mga', 'tawo', 'ang', 'nanulis', 'kaniya', ',', 'apan', 'dunay', 'babaye', 'nga', 'misakay', 'sa', 'dapit', 'nga', 'wa', 'niya', 'mahibaw-i', 'kon', 'diin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,817 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingon', 'nga', 'sa', 'tibuok', 'biyahe', 'gitaptapan', 'ang', 'iyang', 'mga', 'mata', 'sa', 'mga', 'tulisan', 'hinungdan', 'nga', 'wa', 'siya', 'masayod', 'diin', 'sila', 'niagi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,818 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Martes', 'sa', 'kaadlawon', 'na', 'siya', 'nakalingkawas', 'gikan', 'sa', 'gidad-an', '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, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,819 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahatagan', 'na', 'og', 'kopya', 'sa', 'close', 'circuit', 'television', '(', 'CCTV', ')', 'camera', 'ang', 'Talamban', 'police', 'nga', 'maoy', 'ilang', 'gisubay', 'aron', 'mailhan', 'kon', 'kinsa', 'ang', 'maong', 'mga', 'tulisan', 'alang', 'na', 'unya', 'sa', 'himuon', 'nila', 'nga', 'pag', 'gukod', '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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,820 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagsuwat', 'ning', 'mao', 'nga', 'taho', ',', 'wa', 'pay', 'feedback', 'sila', 'si', 'Otida', 'kon', 'unsay', 'kuha', 'sa', 'CCTV', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
6,821 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakig-alayon', 'na', 'usab', 'sila', 'sa', 'Highway', 'Patrol', 'Group-7', 'aron', 'maalarma', 'ang', 'sakyanan', 'nga', 'hangtod', 'karon', 'naa', 'pa', 'sa', 'mga', 'kamot', 'sa', 'mga', 'tulisan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,822 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'konsehal', 'nga', 'kinahanglan', 'nga', 'magmabinantayon', 'sa', 'palibot', 'hilabi', 'na', 'kon', 'mosakay', 'na', 'og', 'sakyanan', ',', 'mas', 'maayong', 'ma-lock', 'dayon', 'ni', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,823 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'ni', 'Tumulak', 'ang', 'publiko', 'nga', 'kon', 'mabantayan', 'ang', 'sakyanan', 'ni', 'Seniedo', 'i-report', 'dayon', 'sa', 'labing', 'duol', 'nga', 'police', 'station', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,824 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'konsehal', 'nagtuo', 'nga', 'local', 'boys', 'ra', 'maoy', 'naghimo', 'sa', 'maong', '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, 0, 0, 0] | cebuaner |
6,825 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'ilang', 'assessment', 'sa', 'tax', 'ug', 'regulatory', 'fees', ',', 'sukad', '2014', ',', 'niabot', 'sa', 'P979,466.50', 'ang', 'utang', 'niini', 'nga', 'buhis', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,826 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nobiyembre', '20', ',', 'nibayad', 'kini', 'og', 'P422,877.50', 'samtang', 'ang', 'kuwang', 'nga', 'kapin', 'P500,000', 'nasabutan', 'pinaagi', 'sa', 'compromise', 'agreement', 'nga', 'bayran', 'kini', 'nila', 'sulod', 'sa', 'unom', 'ka', 'buwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,827 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Agus', 'Resort', 'Development', 'Corp.', 'ang', 'nagdala', 'sa', 'Abuhan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 0, 0, 0, 5, 0] | cebuaner |
6,828 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisuwayan', 'ning', 'mantalaan', 'pagkuha', 'sa', 'habig', 'sa', 'maong', 'kan-anan', 'apan', 'sa', 'pagbisita', 'sa', 'maong', 'dapit', 'sirado', 'na', 'kini', 'sugod', 'niadtong', 'Nobiyembre', '28', 'ug', 'wa’y', 'representante', 'nga', 'niatubang', 'atol', 'sa', 'pagbisita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,829 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'outlet', 'sa', 'Rico’s', 'Lechon', 'gipasirhan', 'sa', 'mayor', 'pipila', 'ka', 'mga', 'buwan', 'ang', 'nakalabay', 'tungod', 'usab', 'sa', 'kakuwang', 'sa', 'business', 'permit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,830 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'tindahan', 'nahimutang', 'luyo', 'sa', 'Camp', 'Sergio', 'Osmeña', ',', 'headquarter', 'sa', 'Police', 'Regional', 'Office', '7', '(', '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, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0] | cebuaner |
6,831 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pasado', 'sa', 'alas', '8', 'sa', 'buntag', 'niadtong', 'Lunes', 'na', 'nadiskobrehan', 'sa', 'management', 'sa', 'tindahan', 'nga', 'gisulod', 'sila', 'og', 'kawatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,832 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'dihang', 'ilang', 'nasubay', ',', 'giingong', 'didto', 'niagi', 'ang', 'mga', 'kawatan', 'sa', 'kwarto', 'sa', 'hotel', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,833 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ila', 'dayon', 'nga', 'gitan-aw', 'ang', 'kuha', 'sa', 'ilang', 'CCTV', 'camera', 'ug', 'dinhi', 'ilang', 'nasuta', 'nga', 'duha', 'ka', 'mga', 'lalake', 'nga', 'nagtaptap', 'sa', 'ilang', 'panagway', 'ang', 'nanguha', 'sa', 'mga', 'butang', 'sa', 'tindahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,834 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Chief', 'Insp.', 'Maria', 'Theresa', 'Macatangay', 'nga', 'wa', 'pa', 'mohatag', 'og', 'detalye', 'ang', 'tag-iya', 'sa', 'Thinking', 'Tools', 'sa', 'pagsuwat', 'ning', 'taho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0] | cebuaner |
6,835 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'nay', 'gidudahan', 'ang', 'Station', '2', 'sa', 'Cebu', 'City', 'Police', 'Office', 'kinsa', 'ang', 'maong', 'mga', 'kawatan', 'apan', 'di', 'pa', 'mahimong', 'ikabutyag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,836 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitug-an', 'ni', 'Benluan', 'nga', 'identified', 'na', 'ang', 'mga', 'nagpaluyo', 'sa', 'maong', 'fake', 'news', 'nga', 'gipakatap', 'sa', 'ilang', 'FB', 'account', 'nga', 'unang', 'nigawas', 'niadtong', 'sayong', 'bahin', 'sa', 'Hulyo', 'ning', 'tuiga', 'diin', 'giingong', 'adunay', 'bomba', 'nga', 'gibilin', 'sa', 'usa', 'ka', 'sari-sari', 'store', 'sa', 'Barangay', 'Basak', ',', 'Lapu-Lapu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0] | cebuaner |
6,837 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasuta', 'nga', 'usa', 'lang', 'ka', 'transistor', 'radio', 'nga', 'gisud', 'og', 'plastic', 'ang', 'wa', 'tuyoa', 'nga', 'nahibilin', 'niadtong', 'lalake', 'nga', 'nagpasilong', 'sa', 'tindahan', 'sa', 'dihang', 'nagpa-abot', 'og', 'kasakyan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,838 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'endorsement', 'gisukipan', 'og', 'kopya', 'sa', 'mga', 'affidavit', 'nga', 'ilang', 'nahipos', 'ug', 'resulta', 'sa', 'gihimong', 'imbestigasyon', 'sa', 'Police', 'Regional', 'Office', '(', 'PRO', ')', '7sa', 'ilang', 'Anti-Cybercrime', 'Group', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 3, 4, 4, 4, 4, 4, 4, 0, 3, 4, 0] | cebuaner |
6,839 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Natala', 'sa', 'draft', 'nga', 'gihimo', 'sa', 'city', 'attorney’s', 'office', 'nga', 'naghatag', 'og', 'authority', 'sa', 'konseho', 'nga', 'kapasakaan', 'og', 'pormal', 'nga', 'kaso', 'ang', 'CFR', 'sa', 'ilang', 'nahimong', 'kalapasan', ',', 'diin', 'ang', 'Lapu-Lapu', 'City', 'magsilbing', 'complainant', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 5, 6, 0, 0, 0] | cebuaner |
6,840 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'na', 'ka', 'mga', 'tawo', 'ang', 'bulontaryong', 'mitugyan', 'sa', 'ilang', 'mga', 'armas', 'nga', 'way', 'lisensiya', 'human', 'nga', 'ang', 'kapulisan', 'sa', 'Talisay', 'nihimog', 'Oplan', 'Katok', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 7, 8, 0] | cebuaner |
6,841 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Oplan', 'Katok', 'usa', 'ka', 'programa', 'sa', 'kapulisan', 'sa', 'nasud', 'diin', 'kadtong', 'mga', 'tawo', 'nga', 'naghupot', 'og', 'armas', 'nga', 'wala', 'ma-renew', 'ang', 'lisensya', 'ilang', 'tuktukon', 'ug', 'pahibaw-on', 'nga', 'anaa', 'sila', 'sa', 'listahan', 'sa', 'kapulisan', 'nga', 'naghupot', 'og', 'armas', 'nga', 'wala', 'ma-renew', 'ug', 'kinahanglan', 'na', 'kining', 'i-renew', 'tungod', 'kay', 'kon', 'di', ',', 'mahimo', 'na', 'silang', 'madakop', 'pinaagi', 'sa', 'search', 'warrant', 'tungod', 'sa', 'paghupot', 'og', 'illegal', 'nga', '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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
6,842 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Supt.', 'Jason', 'Villamater', 'hepe', 'sa', 'Talisay', 'Police', 'Station', 'miingon', 'nga', 'mahimong', 'wa', 'ma-renew', 'ang', 'lisensya', 'sa', 'armas', ',', 'mahimo', 'usab', 'nga', 'nabaligya', 'na', ',', 'gikawat', 'o', 'ba', 'kaha', 'nibalhin', 'na', 'sa', 'pagpuyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,843 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'ni', 'Villamater', 'kadtong', 'mga', 'tawo', 'nga', 'mahimo', 'usab', 'nga', 'i-deposito', 'lang', 'una', 'ang', 'ilang', 'armas', 'ngadto', 'sa', 'ilang', 'buhatan', 'hangtod', 'nga', 'dili', 'ma-renew', 'ang', 'lisensya', 'aron', 'di', 'kini', 'nila', 'dakpon', 'sa', 'umaabot', 'nga', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 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] | cebuaner |
6,844 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'alyas', 'Cañor', ',', '32', ',', 'taga', 'Barangay', 'Lawaan', 'sa', 'nahisgutang', 'syudad', 'nibutyag', 'nga', 'gitoktok', 'siya', 'sa', 'mga', 'pulis', 'ug', 'giawhag', 'nga', 'itahan', 'nalang', 'ang', 'iyang', '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, 1, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,845 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahibaw-an', 'kining', 'aduna', 'siyay', 'armas', 'human', 'gi-report', 'sa', 'barangay', 'tanod', 'nga', 'nagtinir', 'kining', 'og', '.357', 'revolver', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,846 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gipasabot', 'nga', 'depensa', 'lang', 'una', 'kini', 'niya', 'tungod', 'kay', 'adunay', 'naglagot', 'kaniya', 'kaniadto', 'hinungdan', 'nga', 'nipalit', 'siyag', 'armas', 'nga', 'paltik', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,847 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'gitataw', 'ni', 'Villamater', 'nga', 'walay', 'kaso', 'nga', 'ilang', 'ipasaka', 'niadtong', 'bulontaryong', 'mitahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,848 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gani', 'kon', 'mahadlok', 'sila', 'mahimong', 'adto', 'lang', 'sila', 'mosurrender', 'sa', 'ilang', 'armas', 'sa', 'mga', 'barangay', 'opisyal', 'ug', 'sila', 'na', 'ang', 'mokuha', 'niini', 'nga', 'wala', 'gihapoy', 'kasong', 'atubangon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,849 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'gihimo', 'usab', 'sa', 'kapulisan', 'agig', 'pangandam', 'sa', 'umaabot', 'nga', 'Pasko', 'ug', 'Bag-ong', 'Tuig', 'diin', 'adunay', 'mga', 'tawo', 'nga', 'magpabuto', 'sa', 'ilang', '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, 7, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,850 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'usa', 'ka', 'stakeholders', 'public', 'hearing', 'ang', 'gihimo', 'sa', 'Cebu', 'City', 'Hall', 'aron', 'mapaminaw', 'ang', 'habig', 'sa', 'mga', 'negosyante', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,851 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Di', 'mominus', '20', 'ka', 'mga', 'stakeholder', 'ang', 'nitambong', 'sa', 'public', 'hearing', 'diin', 'ilang', 'gipadayag', 'ang', 'ilang', 'sentimento', 'nga', 'maapektuhan', 'sila', 'sa', 'plano', 'ni', 'Cebu', 'City', 'Mayor', 'Tomas', 'Osmeña', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0] | cebuaner |
6,852 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'mipadayag', 'sa', 'iyang', 'kabalaka', 'mao', 'si', 'John', 'James', 'Uy', 'sa', 'Bai', 'Club', ',', 'diin', 'miingon', 'siya', 'nga', 'panahon', 'sa', 'Sinulog', 'mao', 'kini', 'ang', 'peak', 'season', 'nila', 'diin', 'dako', 'og', 'sila', 'og', 'halin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 2, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,853 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'nga', 'Enero', '21', 'ang', 'Siyudad', 'nagtinguha', 'nga', 'mopatuman', 'og', 'liquor', 'ban', 'sud', 'sa', '300', 'metros', 'rota', 'sa', 'Sinulog', 'gikan', 'sa', 'alas', '6:00', 'buntag', 'na', 'sa', 'alas', '6:00', 'buntag', 'sa', 'Enero', '22', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,854 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Uy', 'nagkanayon', 'nga', 'kon', 'ingon', 'niini', 'ang', 'paagi', 'gihangyo', 'nila', 'ang', 'Siyudad', 'nga', 'kon', 'mahimo', 'maminusan', 'ang', 'buhis', 'nga', 'ilang', 'bayran', 'sa', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,855 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gimanduan', 'ang', 'magtiayong', 'Jonathan', 'ug', 'Argentina', 'Amistad', 'sa', 'paghunong', 'og', 'himo', 'ug', 'pamaligya', 'og', 'pabuto', 'sa', 'higayon', 'nga', 'dili', 'makatuman', 'sa', 'requirement', 'sa', 'ahensiya', 'ingon', 'man', 'sa', '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, 1, 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] | cebuaner |
6,856 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kahinumduman', 'nga', 'niadtong', 'Nobiyembre', '22', ',', '2017', 'sa', 'hapon', 'unom', 'katawo', 'lakip', 'na', 'sa', 'magtiayong', 'Amistad', 'ang', 'naangol', 'atol', 'sa', 'pag-ulbo', 'sa', 'ilang', 'himoanan', 'og', 'pabuto', 'diin', 'tulo', 'niini', 'mga', 'menor', 'de', 'edad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,857 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Amistad', 'di', 'sakop', 'sa', 'Rambolet', ',', 'apan', 'kanhi', 'miyembro', 'kaniadto', 'ug', 'wa', 'makapa-renew', 'sa', 'ilang', 'lisensiya', 'ning', 'tuiga', 'gumikan', 'sa', 'suliran', 'sa', 'panalapi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,858 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'naasoyng', 'paagi', 'mamahimong', 'legal', 'ang', 'ilang', 'pag-manufacture', 'ug', 'pagbaligya', 'og', 'firecracker', 'ug', 'pyrotechnic', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,859 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikatakda', 'nga', 'karong', 'Disyembre', '15', 'magsugod', 'na', 'og', 'display', 'sa', 'mga', 'pabuto', 'diha', 'daplin', 'sa', 'karsada', 'sa', 'barangay', 'Babag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
6,860 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'lungsod', 'sa', 'Liloan', 'ug', 'City', 'of', 'Naga', 'giila', 'nga', 'top', 'performing', 'nga', 'lungsod', 'ug', 'component', 'city', 'sa', 'Cebu', 'Province', 'nga', 'dunay', 'aktibo', 'kaayong', 'Anti-Drug', 'Abuse', 'Councils', '(', 'ADAC', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 5, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0] | cebuaner |
6,861 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'lungsod', 'sa', 'San', 'Remegio', 'ug', 'Consolacion', 'nagsunod', 'nga', 'ikaduha', 'ug', 'ikatulo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 6, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
6,862 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'mao', 'nga', 'mga', 'lungsod', ',', 'laing', 'giila', 'tungod', 'sa', 'maayo', 'nilang', 'mga', 'Madac', 'mao', 'ang', 'Pinamungajan', ',', 'Medellin', ',', 'Cordova', ',', 'Tabuelan', ',', 'Santander', ',', 'Argao', ',', 'Sogod', ',', 'Madridejos', 'ug', 'Barili', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 3, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 0] | cebuaner |
6,863 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'mga', 'LGU', ',', 'ang', 'local', 'police', 'stations', 'sa', 'mga', 'lungsod', 'ug', 'siyudad', 'sa', 'lalawigan', 'giila', 'usab', 'sa', 'ilang', 'kampanya', 'batok', 'sa', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,864 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Consolacion', 'Police', 'Station', 'maoy', 'nag-una', 'sa', '12', 'ka', 'municipal', 'police', 'units', 'nga', 'maayo', 'og', 'lakat', 'sa', 'ilang', 'kampanya', 'batok', 'sa', 'illegal', 'drug', 'campaign', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,865 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Laing', 'police', 'units', 'nga', 'giila', 'sa', 'Cebu', 'Provincial', 'Government', 'naglakip', 'sa', 'San', 'Remegio', ',', 'Liloan', ',', 'Boljoon', ',', 'Alcantara', ',', 'Moalboal', ',', 'Compostela', ',', 'Ronda', ',', 'Alcoy', ',', 'Balamban', ',', 'Sibonga', 'ug', 'Dumanjug', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0] | cebuaner |
6,866 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Carmen', 'Remedios', 'Durano-Meca', ',', 'pangu', 'sa', 'Cebu', 'Provincial', 'Anti-Drug', 'Abuse', 'Office', '(', 'CPADAO', ')', ',', 'niingon', 'nga', 'ang', 'mga', 'award', 'nag-ila', 'sa', 'maayong', 'mga', 'buhat', 'ug', 'programa', 'nga', 'gipatuman', 'sa', 'mga', 'LGU', 'ug', 'kapulisan', 'batok', 'sa', 'illegal', 'drugs', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,867 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Naglaum', 'si', 'Meca', 'nga', 'sunod', 'tuig', 'dugang', 'LGUs', 'ang', 'aktibong', 'mopatuman', 'sa', 'ilang', 'anti-illegal', 'drugs', 'program', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,868 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisugdan', 'na', 'pagtrabaho', 'Department', 'of', 'Engineering', 'and', 'Public', 'Works', '(', 'DEPW', ')', 'ang', 'lungag', 'nga', 'nitumaw', 'sa', 'dan', 'Juana', 'Osmeña', ',', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
6,869 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'unom', 'ka', 'mga', 'trabahante', 'sa', 'DEPW', 'ang', 'nagsugod', 'na', 'pag-ayo', 'sa', 'imburnal', 'nga', 'aduna’y', 'gilapdon', 'nga', 'duha', 'ka', 'metros', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
6,870 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'usab', 'ka', 'mga', 'traffic', 'enforcers', 'ang', 'gipakatap', 'sa', 'maong', '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, 0] | cebuaner |
6,871 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna’y', 'himuong', 'bag-o', 'nga', 'imburnal', 'ang', 'himuon', 'sa', 'maong', 'dapit', 'ubos', 'sa', 'tubo', 'nga', 'sa', 'ilawom', 'usab', 'sa', 'lungag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,872 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pagkahugno', 'sa', 'imburnal', 'nga', 'nimugna', 'og', 'lungag', 'nakapabalaka', 'kay', 'gituohan', 'kini', 'nga', 'sinkhole', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,873 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasuta', 'sa', 'Mines', 'and', 'Geosciences', 'Burea', '(', 'MGB', ')', 'nga', 'nahugno', 'nga', 'imburnal', ',', 'di', 'sinkhole', ',', 'ang', 'nakaingon', 'sa', 'lungag', '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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,874 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'sa', 'duha', 'ka', 'kusog', 'nga', 'mga', 'linog', 'sa', 'Central', 'Visayas', ',', 'nabantayan', 'ang', 'pagtumaw', 'sa', 'mga', 'sinkhole', 'sa', 'daghang', 'mga', 'dapit', '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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,875 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'mga', 'sinkhole', 'ang', 'nakaplagan', 'sa', 'dakbayan', 'sa', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,876 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Di', 'na', 'kini', 'ikahibung', 'tungod', 'sa', 'anupog', 'nga', 'matang', 'sa', 'yuta', 'dinhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,877 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pangulo', 'sa', 'mga', 'fish', 'vendor', 'sa', 'Calderon', 'street', 'sa', 'may', 'Carbon', 'Public', 'Market', 'nasagmuyo', 'sa', 'pag-apil', 'sa', 'iyang', 'ngalan', 'isip', 'usa', 'sa', 'mga', 'mastermind', 'sa', 'pag-ambush', 'patay', 'kang', 'Kapitan', '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, 5, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0] | cebuaner |
6,878 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'abogado', 'ni', 'Juvelyn', 'Gomez', ',', 'wa', 'kini', 'motibo', 'ug', 'labaw', 'nang', 'wa', 'kini', 'kapasidad', 'sa', 'pagpapatay', 'sa', 'kapitan', 'ug', 'andam', 'siyang', 'makig-alayon', 'sa', 'mga', 'awtoridad', 'aron', 'paglimpyo', 'sa', 'iyang', 'ngalan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,879 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Napasakaan', 'na', 'og', 'kasong', 'murder', 'si', 'Jimmy', 'Largo', ',', 'gitumbok', 'nga', 'usa', 'sa', 'mga', 'suspek', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,880 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Largo', 'gikiha', 'sab', 'og', 'attempted', 'murder', 'kay', 'lakip', 'nga', 'sakay', 'sa', 'gibanhigan', 'nga', 'sakyanan', 'ang', 'kapuyo', 'sa', 'kapitan', 'nga', 'si', 'Jocilyn', '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, 0, 0, 0, 1, 2, 0] | cebuaner |
6,881 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Laing', 'mga', 'kasong', 'ilegal', 'nga', 'paghupot', 'og', 'armas', ',', 'explosives', 'ug', 'kalapasan', 'sa', 'ilegal', 'nga', 'paghupot', 'og', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,882 | 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', 'abogado', 'ni', 'Gomez', 'nga', 'si', 'Ferdinand', 'Gujilde', ',', 'di', 'makahimo', 'ang', 'iyang', 'kliyente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,883 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', ',', 'wa', 'say', 'kapasidad', 'pagkuha', 'sa', 'serbisyo', 'sa', 'gunman', 'tungod', 'sa', 'kagamay', 'lang', 'sa', 'kita', '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] | cebuaner |
6,884 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tinguha', 'ni', 'Gomez', 'nga', 'malimpyohan', 'ang', 'iyang', 'ngalan', 'human', 'siya', 'gidudahan', 'nga', 'nipaluyo', 'sa', 'kamatayon', 'ni', 'Rupinta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,885 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Laing', 'saksi', 'sa', 'pagbanhig', 'nakakita', 'kang', 'Largo', 'nga', 'sakay', 'sa', 'motorsiklo', 'ug', 'nisunod', 'sa', 'sakyanan', 'sa', 'kapitan', 'gikan', 'sa', 'Barangay', 'Ermita', 'sa', 'wa', 'pa', 'mahitabo', 'ang', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,886 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'ang', 'nakapalig-on', 'sa', 'pangangkon', 'sa', 'PRO', '7', 'nga', 'si', 'Largo', 'ang', 'nagbanhig', 'sa', 'kapitan', 'sukwahi', 'sa', 'pagpanghimakak', 'niini', 'nga', 'naa', 'ra', 'siya', 'sa', 'Carbon', 'sa', 'dihang', 'nahitabo', 'ang', '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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
6,887 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingong', 'nagsunod', 'si', 'Largo', 'hangtod', 'sa', 'dapit', 'sa', 'Liloan', 'diin', 'nahitabo', 'ang', 'pagpamanhig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,888 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nailhan', 'na', 'usab', 'nila', 'ang', 'laing', 'gunman', 'nga', 'kauban', 'ni', 'Largo', 'nga', 'posibling', 'masikop', 'na', 'sa', 'di', 'madugay', 'nga', 'usa', 'sa', 'mga', 'gitudlo', 'sa', 'mga', 'saksi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,889 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'giila', 'ang', 'mastermind', 'nga', 'gihulagway', 'ni', 'Espino', 'nga', 'wa', 'pay', 'kasayuran', 'nga', 'sila', 'ang', 'gidudahan', 'sa', 'kapulisan', 'nga', 'naa', 'pa', 'sa', 'syudad', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,890 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'na', 'modawat', 'ang', 'Special', 'Investigation', 'Task', 'Group', 'sa', 'pamahayag', 'ni', 'Largo', 'nga', 'nasayod', 'siya', 'sa', 'nagpaluyo', 'sa', 'pagpatay', 'sa', 'kapitan', 'diin', 'giduot', 'niini', 'nga', 'dunay', 'bulok', 'politika', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,891 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DI', 'tugotan', 'ni', 'Mayor', 'Tomas', 'Osmeña', 'nga', 'ang', 'mohulip', 'ni', 'Kapitan', 'Felicisimo', '“Imok”', 'Rupinta', 'sa', 'Barangay', 'Ermita', 'adunay', 'giingong', 'sama', 'nga', 'kalidad', 'sa', 'kanhi', '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, 1, 2, 2, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,892 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Miral', 'nakakuha', 'og', '3,336', 'sa', 'eleksyon', 'niadtong', '2013', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,893 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'nagkanayon', 'nga', 'sa', 'pagdumala', 'ni', 'Rupinta', 'sa', 'Ermita', 'sulod', 'sa', 'kapin', '15', 'ka', 'tuig', ',', 'ang', 'kani', 'mga', 'small', 'drug', 'pusher', 'inanay', 'nga', 'nilipang', 'ug', 'nahimong', 'bantugan', 'nga', 'drug', 'lord', 'sa', '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, 1, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
6,894 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihulagway', 'ni', 'Osmeña', 'nga', 'adunay', 'power', 'struggle', 'nga', 'mahitabo', 'sa', 'barangay', 'Ermita', 'human', 'namatay', 'ang', 'ilang', 'gikonsidera', 'nga', 'lider', 'ug', 'amahan', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,895 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dili', 'na', 'unya', 'maglisod', 'og', 'sakay', 'ang', 'mga', 'pasahero', 'sa', 'mga', 'taxi', 'cab', 'dinhi', 'sa', 'lalawigan', 'sa', 'Sugbo', 'gumikan', 'sa', 'pag-abot', 'sa', 'bag-ong', 'teknolohiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,896 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Metro', 'Cebu', 'Taxi', 'Operators', 'Association', '(', 'MCTOA', ')', 'nipalambo', 'sa', 'ilang', 'serbisyo', 'aron', 'ilang', 'matupngan', 'ang', 'pamaagi', 'sa', 'ilang', 'kompetensiya', 'sa', 'industriya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,897 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Richard', 'Cabucos', ',', 'presidente', 'sa', 'MCTOA', ',', 'nagkanayon', 'nga', 'gumikan', 'sa', 'panginahanglan', 'sa', 'ilang', 'mga', 'pasahero', ',', 'nibubo', 'pa', 'sila', 'og', 'dugang', 'puhonan', 'aron', 'mapasayon', 'pa', 'sa', 'ilang', 'mga', 'pasahero', 'ang', 'pagsakay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 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 |
6,898 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Para', 'ma', 'equal', 'level', 'mi', 'sa', 'bag-ong', 'technology', 'sa', 'Grab', 'ug', 'Uber', 'mao', 'nga', 'wala', 'mi', 'mahimo', 'kon', 'dili', 'among', 'parangan', 'ang', 'ilang', 'teknolohiya', 'ug', 'kami', 'duna', 'sad', 'mi', 'kaugalingon', 'nga', 'apps', 'nga', 'para', 'sa', 'pasahero', 'kay', 'mao', 'may', 'ilang', 'demand', ',', '”', 'matod', 'ni', 'Cabucos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
6,899 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibawo', 'ni', 'Ybañez', 'nga', 'sama', 'sa', 'Grab', 'ug', 'Uber', ',', 'ang', 'MICAB', 'ma', 'download', 'usab', 'gikan', 'sa', 'playstore', 'para', 'sa', 'android', 'phone', 'ug', 'app', 'store', 'para', 'sa', 'mga', 'Iphone', 'user', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 3, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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