Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,100 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'tagdumala', 'sa', 'jail', 'nitakda', 'og', 'Family', 'Day', 'sa', 'Disyembre', '25', 'ug', '26', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,101 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'CPDRC', 'acting', 'warden', 'Audesti', 'Miguel', 'niingon', 'nga', 'ang', 'mga', 'pamilya', 'sa', 'mga', 'binilanggo', 'di', 'na', 'kinahanglan', 'magdala', 'tungod', 'kay', 'ang', 'mga', 'piniriso', 'maoy', 'mag-andam', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,102 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'nga', 'ilang', 'hatagan', 'og', 'kahigayunan', 'ang', 'mga', 'binilanggo', 'nga', 'makasaulog', 'uban', 'sa', 'ilang', '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] | cebuaner |
6,103 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'bags', 'ug', 'regalo', 'susihon', 'aron', 'malikayan', 'ang', 'pagpayuhot', 'og', 'mga', 'kontrabando', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,104 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisugyot', 'ni', 'Miguel', 'nga', 'puston', 'ang', 'mga', 'regalo', 'nga', 'mas', 'sayon', 'alang', 'sa', 'inspection', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,105 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'si', 'Gobernador', 'Hilario', 'Davide', 'II', 'nimando', 'kang', 'Miguel', 'sa', 'pagsuwat', 'sa', 'Cebu', 'Provincial', 'Police', 'Office', 'aron', 'mangayog', 'dugang', 'mga', 'pulis', 'alang', 'sa', 'Disyembre', '25', 'ug', '26', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 2, 0, 0, 1, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,106 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'CPDRC', 'nitugot', 'lang', 'og', 'tulo', 'ka', 'mga', 'bisita', 'matag', 'binilanggo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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] | cebuaner |
6,107 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'CPDRC', ',', 'naila', 'sa', 'kalibotan', 'tungod', 'sa', 'iyang', 'dancing', 'inmates', ',', 'maoy', 'sentro', 'ug', 'labing', 'dako', 'nga', 'bilanggoan', 'nga', 'nahimong', 'kontrobersyal', 'tungod', 'sa', 'pagpayuhot', 'og', 'gidiling', 'drugas', 'ug', 'gadgets', 'sama', 'sa', 'cellphones', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,108 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidakop', 'ang', '42-anyos', 'nga', 'lalake', 'human', 'kini', 'giingong', 'nanglugos', 'sa', '16-anyos', 'nga', 'babaye', 'sa', 'usa', 'ka', 'barangay', 'sa', 'lungsod', 'sa', 'Tabogon', ',', 'Sugbo', 'niadtong', 'Sabado', 'sa', 'kaadlawon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
6,109 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'usab', 'sa', 'imbestigasyon', 'sa', 'kapulisan', 'nga', 'adunay', 'kasaysayan', 'ang', 'suspek', 'human', 'usab', 'kini', 'giingong', 'niunay', 'og', 'lugos', 'sa', 'iyang', 'kaugalingong', 'anak', 'apan', 'wa', 'mosang-at', 'og', 'kaso', 'ang', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,110 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakisusi', 'sa', 'kapulisan', ',', 'hubog', 'si', 'Erik', 'dihang', 'nahitabo', 'ang', 'insidente', 'alas', '12:30', 'sa', 'kaadlawon', 'sa', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,111 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'nga', 'ang', 'biktima', 'nga', 'si', 'Elena', 'nitambong', 'sa', 'disco', 'nga', 'gipahigayon', 'sa', 'basketball', 'court', 'ug', 'nagpakuyog', 'kini', 'sa', 'iyang', 'lalake', 'nga', 'higala', 'aron', 'mangihi', 'sa', 'kalibunan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,112 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingon', 'nga', 'nagbarog', 'layo', 'sa', 'giihian', 'ni', 'Elena', 'iyang', 'amigo', 'ug', 'kalit', 'lang', 'gisumbag', 'ang', 'biktima', 'ni', 'Erik', 'ug', 'gidala', 'kini', '120', 'metros', 'gikan', 'sa', 'giihian', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,113 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'na', 'makaangal', 'si', 'Elena', 'tungod', 'kay', 'gitionan', 'siya', 'niini', 'og', 'kutsilyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,114 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'na', 'makaangal', 'si', 'Elena', 'tungod', 'kay', 'gitionan', 'siya', 'niini', 'og', 'kutsilyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,115 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisultihan', 'niya', 'iyang', 'higala', 'sa', 'panghitabo', 'ug', 'ilaha', 'kining', 'gi-report', 'dayon', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,116 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'hot', 'pursuit', 'operation', ',', 'nasikop', 'ang', 'gituhoang', 'rapist', 'human', 'ikaduhang', 'kining', 'higayon', 'nga', 'iyang', 'gibuhat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,117 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'CHR', '7', 'head', 'Arvin', 'Odron', 'nga', 'mamahimong', 'maabusahan', 'ang', 'financial', 'assistance', 'ug', 'nipasabot', 'kini', 'nga', 'di', 'tungod', 'kay', 'gihatagan', 'og', 'katungod', 'ang', 'kapulisan', 'nga', 'mohupot', 'og', 'armas', ',', 'anaa', 'na', 'usab', 'kini', 'katungod', 'nga', 'mopatay', '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, 3, 4, 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, 0, 0, 0, 0, 0] | cebuaner |
6,118 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'P', '/', 'Supt.', 'Artemio', 'Ricabo', ',', 'deputy', 'city', 'director', 'for', 'administration', ',', 'sa', 'iyang', 'bahin', 'nagkanayon', 'nga', 'ang', 'legal', 'assistance', 'makapadasig', 'kanila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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] | cebuaner |
6,119 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakakita', 'ka', 'ba', 'sa', 'dakong', 'kabag-ohang', 'sa', 'dan', 'Gen.', 'Maxilom', ',', 'dakbayan', 'sa', 'Sugbo', 'ilabina', 'kon', 'gabii', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0] | cebuaner |
6,120 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'studs', 'mosiga', 'pinaagi', 'sa', 'solar', 'cell-powered', 'light', 'gikan', 'sa', 'diodes', 'nga', 'gilubong', 'sa', 'dan', 'nga', 'motabang', 'sa', 'paggiya', 'sa', 'mga', 'drayber', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,121 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gatusan', 'ka', 'studs', 'ang', 'gibutang', 'sa', 'dan.', 'Nasayod', 'ka', 'ba', 'pila', 'ang', 'matag', 'usa', '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 |
6,122 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Engr.', 'Ador', 'Canlas', ',', 'DPWH', '7', 'Regional', 'Director', ',', 'niingon', 'nga', 'ang', 'studs', 'gikan', 'sa', 'South', 'Korea', 'ug', 'matag', 'niini', 'mobalor', 'og', 'P7,000', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,123 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'sa', 'pangulo', 'sa', 'BMO', 'sa', 'Suba', 'nga', 'libre', 'ang', 'konsultasyon', 'sa', 'mga', 'nagpuyo', 'sa', 'Suba', 'ug', 'gihatagan', 'sila', 'og', 'libreng', 'tambal', 'ug', 'bitamina', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,124 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Libre', 'usab', 'ang', 'pag-ibot', 'sa', 'mga', 'ngipon', 'nga', 'di', 'na', 'maluwas', 'gumikan', 'sa', 'grabeng', 'daot', ',', 'tambal', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,125 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihatagan', 'usab', 'og', 'libreng', 'eyeglasses', 'ang', 'mga', 'nagpakonsulta', 'gumikan', 'sa', 'ilang', 'deperensiyado', 'nga', 'mata', 'kansang', 'grado', 'moabot', 'hangtod', '350', 'pinangulohan', 'ni', 'Dr.', 'Joy', 'Maranga', 'Versalez', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 2, 2, 0] | cebuaner |
6,126 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Maranga', 'kinsa', 'nabantog', 'sa', 'iyang', 'pagtabang', 'sa', 'mga', 'labaw', 'nga', 'nanginahanglan', 'nagkanayon', 'nga', 'nanghatag', 'usab', 'sila', 'og', 'daghang', 'mga', 'tsinelas', 'alang', 'sa', 'mga', 'residente', 'sa', 'Suba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,127 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'plano', 'nga', 'himuon', 'ang', 'medical-dental-optical', 'mission', 'sa', 'iyang', 'barangay', 'matag', 'tulo', 'ka', 'buwan', 'aron', 'mahatagan', 'og', 'maayong', 'panglawas', ',', 'nindot', 'nga', 'pahiyom', 'ug', 'tin-aw', 'nga', 'panan-aw', 'ang', 'mga', 'tawo', 'sa', 'Suba', 'diin', 'ang', 'mga', 'silingang', 'molupyo', 'makapahimulos', 'usab', 'sa', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
6,128 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihangop', 'ug', 'gipasigarbo', 'sa', 'Barangay', 'Adlaon', 'ang', 'ilang', 'bag-ong', 'kalampusan', 'may', 'labot', 'sa', 'pagpatunhay', 'sa', 'kahapsay', ',', 'kalinaw', 'ug', 'pagsulbad', 'sa', 'mga', 'krimen', 'nga', 'nahitabo', 'sa', 'ilang', 'barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,129 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Barangay', 'Kapitan', 'Nieves', 'Narra', 'sa', 'Adlaon', 'nihulagway', 'nga', 'way', 'kabutangan', 'sa', 'iyang', 'kalipay', 'sanglit', 'ang', 'iyang', 'barangay', 'nakahatag', 'og', 'maayong', 'imahe', '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, 1, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,130 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'niya', 'nga', 'kaniadto', ',', 'ang', 'ilang', 'barangay', 'makaduso', 'og', 'kapin', '100', 'ka', 'kaso', 'sa', 'barangay', 'ngadto', 'sa', 'DILG', 'apan', 'niining', 'bag-o', ',', 'nag-anam', 'og', 'kaubos', 'ang', 'mga', 'kaso', 'sa', 'barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,131 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitug-an', 'niya', 'nga', 'karong', 'tuiga', ',', 'aduna', 'lay', '18', 'ka', 'kaso', 'ang', 'natala', 'sa', 'ilang', 'barangay', 'nga', 'nasulbad', 'sa', 'ilang', 'mga', 'lupon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,132 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kasamtangan', 'pa', 'usab', 'nga', 'gipangita', 'ang', 'duha', 'ka', 'mga', 'suspek', 'sa', 'insidente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,133 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'biktima', 'naila', 'nga', 'si', 'Sergster', 'Uy', ',', '38', ',', 'residente', 'sa', 'Sitio', 'Soquiete', ',', 'T.', 'Padilla', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 5, 6, 6, 6, 6, 0] | cebuaner |
6,134 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakisusi', 'sa', 'kapulisan', 'nagkuyog', 'ang', 'tulo', 'alas', '3:45', 'sa', 'hapon', 'dihang', 'naglakaw', 'kini', 'sa', 'naasoy', 'nga', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,135 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkalalis', 'ang', 'tulo', 'ug', 'nibunot', 'si', 'Nudalo', 'og', 'armas', 'sa', 'iyang', 'hawak', 'ug', 'gipusil', 'si', 'Uy', 'sa', 'luyo', 'sa', 'ulo', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
6,136 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dali', 'nga', 'niikyas', 'ang', 'duha', 'samtang', 'nakita', 'sa', 'crime', 'scene', 'ang', 'basiyo', 'sa', 'bala', 'sa', 'kalibre', '45', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,137 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugdan', 'sa', 'pagtukod', 'sa', 'ang', 'bag-ong', 'edipisyo', 'sa', 'Technical', 'Vocational', 'School', 'sa', 'dakbayan', 'sa', 'Talisay', 'ug', 'subhan', 'atol', 'sa', 'Charter', 'Day', 'karong', 'Enero', '12', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 5, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0] | cebuaner |
6,138 | 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', 'ni', 'City', 'Engineer', 'Gamaliel', 'Vicente', ',', 'Jr.', 'hinungdan', 'nga', 'sugdan', 'na', 'nila', 'paglimpyo', 'ang', 'karaang', 'Lagtang', 'Public', 'Market', 'nga', 'bag-o', 'lang', 'gibiyaan', 'sa', 'mga', 'nagpuyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,139 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'giklaro', 'ni', 'Vicente', 'nga', 'ang', 'atubangan', 'lang', 'nga', 'edepisyo', 'ang', 'ilang', 'gamiton', 'ug', 'himuong', 'tunghaan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,140 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'nga', 'dili', 'tanang', 'lugar', 'sa', 'merkado', 'sa', 'lagtang', 'ang', 'ilang', 'ukopahon', 'tungod', 'kay', 'dako', 'ra', 'usab', 'kini', 'kaayo', 'ug', 'dili', 'pa', 'usab', 'kaayo', 'daghan', 'nga', 'kurso', 'ang', 'gitanyag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,141 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Engr.', 'Vicente', 'nga', 'dul-an', 'sa', 'usa', 'ka', 'milyon', 'ka', 'pesos', 'ang', 'ilang', 'gi-budget', 'alang', 'pagpalambo', 'sa', 'maong', 'merkado', 'ngadto', 'na', 'sa', 'pagka-eskuylahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,142 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Vicente', 'nga', 'dali', 'ra', 'kining', 'mahuman', 'tungod', 'kay', 'classrooms', 'raman', 'ang', 'ilang', 'himuon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,143 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mayor', 'Eduardo', 'Gullas', 'nimando', 'nga', 'ipadali', 'ang', 'pagtrabaho', 'sa', 'lugar', 'aron', 'maapil', 'kini', 'sa', 'pagsubo', 'karong', 'Enero', '12', 'dungan', 'sa', 'merkado', 'sa', 'Tabunok', 'atol', 'sa', 'charter', 'day', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 7, 8, 0] | cebuaner |
6,144 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mayor', 'Eduardo', 'Gullas', 'nimando', 'nga', 'ipadali', 'ang', 'pagtrabaho', 'sa', 'lugar', 'aron', 'maapil', 'kini', 'sa', 'pagsubo', 'karong', 'Enero', '12', 'dungan', 'sa', 'merkado', 'sa', 'Tabunok', 'atol', 'sa', 'charter', 'day', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 7, 8, 0] | cebuaner |
6,145 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kinahanglan', 'nga', 'moduso', 'og', 'counter-affidavit', 'si', 'Bise', 'Mayor', 'Alan', 'Bucao', 'ug', 'siyam', 'ka', 'mga', 'konsehal', 'sa', 'dakbayan', 'sa', 'Talisay', 'bahin', 'sa', 'kasong', 'kriminal', 'ug', 'administratiba', 'nga', 'gipasaka', 'batok', 'kanila', 'sa', 'Ombudsman', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
6,146 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'miaging', 'buwan', ',', 'si', 'Bucao', 'ug', 'sila', 'si', 'Konsehal', 'Socrates', 'Fernandez', ',', 'Arturo', 'Bas', ',', 'Gail', 'Restauro', ',', 'Manuel', 'Cabriana', ',', 'Rudolfo', 'Cabigas', ',', 'Valeriano', 'Ylanan', ',', 'Richard', 'Francis', 'Aznar', ',', 'Julian', 'Daan', 'ug', 'Raul', 'Cabañero', ',', 'kinsa', 'ABC', 'president'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 0, 1, 2, 0, 0, 3, 0] | cebuaner |
6,147 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'human', 'ang', 'CCT-FEDHOA', 'nasagmuyo', 'sa', 'giingong', 'giyak-an', 'sa', 'konseho', 'ang', 'ilang', 'hangyo', 'nga', 'ma-accredit', 'na', 'ang', 'ilang', 'pederasyon', ',', 'apan', 'niabot', 'na', 'lang', 'ang', '45', 'ka', 'adlaw', 'walay', 'nadawat', 'grupo', 'nga', 'tubag', 'sa', 'ilang', 'mga', 'pangutana', 'kon', 'unsa', 'nay', 'kalamboan', 'sa', 'ilang', 'hangyo', 'sa', 'konseho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,148 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'duha', 'ka', 'mga', 'konsehal', 'ang', 'wala', 'malakip', 'sa', 'kaso', 'nga', 'sila', 'si', 'Konsehal', 'Doroteo', 'Emit', 'ug', 'Konsehal', 'Jojo', 'Bacaltos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0] | cebuaner |
6,149 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'kamandoan', 'sa', 'nadawat', 'niadtong', 'Biyernes', 'sa', 'miaging', 'semana', ',', 'diin', 'gihatagan', 'lang', 'silag', 'napulo', 'ka', 'adlaw', 'sa', 'pagtubag', 'human', 'makita', 'sa', 'Ombudsman', 'nga', 'adunay', 'igong', 'basehanan', 'nga', 'mapadayon', 'ang', 'inbestigasyon', 'alang', 'sa', 'kasong', 'kriminal', 'ug', 'kasong', 'administratiba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,150 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wala', 'usab', 'sila', 'hatagig', 'higayon', 'nga', 'makaduso', 'ug', 'bisan', 'unsang', 'motion', 'o', 'kaha', 're-investigation', 'bugtong', 'mando', 'sa', 'Ombudsman', 'mao', 'lang', 'ang', 'pagduso', 'na', 'sa', 'ilang', 'counter', 'affidavit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,151 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', ',', 'si', 'Bucao', 'nipahibawo', 'nga', 'karong', 'adlawa', 'moduso', 'na', 'sila', 'sa', 'hiniusang', 'counter-affidavit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,152 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giunay', 'pagluba', 'ang', 'manghod', 'sa', 'iyang', 'maguwang', 'human', 'nagbikil', 'ang', 'magsuon', 'samtang', 'nagtagay', 'sud', 'sa', 'ilang', 'balay', 'didto', 'sa', 'Barangay', 'Poblacion', 'Viaje', ',', 'lungsod', 'sa', 'Batuan', 'niadtong', 'Huwebes', 'sa', 'hapon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0] | cebuaner |
6,153 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'ang', 'biktima', 'nga', 'si', 'Zenon', 'Palingcod', ',', '57', ',', 'samtang', 'ang', 'iyang', 'maguwang', 'si', 'Filomino', 'Palingcod', ',', '59', ',', 'maoy', 'gitumbok', 'nga', 'suspek', 'ug', 'samang', 'mga', 'mag-uuma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,154 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakisusi', 'sa', 'kapulisan', ',', 'nasayran', 'nga', 'nag-uban', 'ang', 'magsuon', 'og', 'tagay', 'sud', 'sa', 'ilang', 'balay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,155 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'nagkalami', 'ang', 'tagay', 'sa', 'duha', 'ug', 'samang', 'nakainom', 'kalit', 'lang', 'nga', 'nagmaoy', 'ang', 'biktima', 'ug', 'sa', 'way', 'igong', 'rason', ',', 'gipuspusan', 'ang', 'suspek', 'gamit', 'ang', 'kahoy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,156 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nainit', 'usab', 'ang', 'suspek', 'sa', 'gibuhat', 'sa', 'iyang', 'manghod', ',', 'mikuha', 'kinig', 'kutsilyo', 'ug', 'nibawos', 'pinaagi', 'sa', 'pagluba', 'sa', 'iyang', 'manghod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,157 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', '55', 'anyos', 'nga', 'babaye', 'ang', 'gidunggab', 'patay', 'sa', 'Barangay', 'Magsaysay', 'ning', 'lungsod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0] | cebuaner |
6,158 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'biktima', 'mao', 'si', 'Alicia', 'Ompod', ',', '55', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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] | cebuaner |
6,159 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'suspek', 'mao', 'si', 'Junrey', 'Tapaya', ',', '25', ',', 'nagpuyo', 'sa', 'samang', 'barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,160 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'nga', 'miadto', 'sa', 'balay', 'sa', 'suspek', 'ang', 'biktima', 'ug', 'dunay', 'komusyon', 'nga', 'nahitabo', 'apan', 'wa', 'masayri', 'ang', 'hinungdan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,161 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidunggab', 'sa', 'suspek', 'ang', 'biktima', 'gamit', 'ang', '‘pisaw’', 'nga', 'dunay', 'sukod', 'nga', '16', 'pulgada', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,162 | 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', 'sa', 'Abuyog', ',', 'Leyte', 'ang', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 6, 0, 0, 0] | cebuaner |
6,163 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'gideklarar', 'kini', 'sa', 'doktor', '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] | cebuaner |
6,164 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Patay', 'ang', 'usa', 'ka', 'mananagat', 'human', 'gidunggab', 'didto', 'sa', 'Barangay', 'Vicente', 'ning', 'lungsod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0] | cebuaner |
6,165 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'ang', 'biktima', 'nga', 'si', 'Mario', 'Desoyot', ',', '45', ',', 'mananagat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
6,166 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'suspek', 'mao', 'si', 'Nelson', 'Campomanes', ',', '48', ',', 'usa', 'ka', 'construction', 'worker', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
6,167 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasayran', 'nga', 'dunay', 'nahitabong', 'panaglalis', 'sa', 'duha', 'nga', 'miresulta', 'sa', 'komusyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,168 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mikuhag', 'kabilya', 'ang', 'suspek', 'ug', 'giduslak', 'sa', 'makadaghang', 'higayon', 'ang', 'biktima', 'kinsa', 'dali', 'nga', 'gidala', 'sa', 'tambalanan.', 'Gideklarar', 'kini', 'nga', 'patay', 'na', 'pag-abot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,169 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nalandig', 'sa', 'tambalanan', 'ang', 'usa', 'ka', '16-anyos', 'nga', 'batang', 'lalake', 'human', 'aksidente', 'siya', 'nga', 'napusil', 'sa', 'iyang', 'manghod', 'niadtong', 'Sabado', 'sa', 'gabii', 'sa', 'Barangay', 'Don', 'Pedro', 'Rodriguez', ',', 'dakbayan', 'sa', 'Bogo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
6,170 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'biktima', 'nga', 'naila', 'nga', 'si', 'Leexander', 'Flores', 'Lauron', ',', 'kinsa', 'nakaangkon', 'og', 'samad', 'pinusilan', 'sa', 'dughan', ',', 'human', 'siya', 'napusilan', 'sa', 'iyang', 'manghud', 'gamit', 'ang', 'improvised', '.22', 'nga', 'pistola', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,171 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iya', 'sa', 'ilang', 'uyoan', 'ang', 'maong', 'armas', 'apan', 'wala', 'kini', 'mahinganli', 'atol', 'sa', 'pakisusi', 'sa', 'kapulisan', 'pinaagi', 'ni', 'PO1', 'Alexis', 'Camelo', 'Villacrusis', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0] | cebuaner |
6,172 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'wala', 'pa', 'ang', 'insidente', ',', 'alas', '9:30', 'sa', 'gabii', ',', 'nagduwa', 'og', 'armas', 'si', 'Leexander', 'ug', 'iyang', 'igsuon', 'nga', 'si', 'Gerald', ',', '14', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 0, 0, 0] | cebuaner |
6,173 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Atol', 'sa', 'ilang', 'pagduwa', ',', 'gitutok', 'ni', 'Gerald', 'ang', 'maong', 'pistola', 'sa', 'iyang', 'maguwang', 'ug', 'gikablit', 'ang', 'gato', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,174 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niingon', 'si', 'Pumatong', 'nga', 'ang', 'importers', 'mideklarar', 'sa', 'mga', 'kargamento', 'nga', 'ceramic', 'tiles', 'apan', 'nakita', 'sa', 'X-ray', 'ug', 'sa', 'pasiunang', 'pakisusi', 'nga', 'bugas', 'ang', 'sulod', 'sa', 'container', 'vans', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,175 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ubos', 'sa', 'balaod', ',', 'ang', 'mga', 'importers', 'kinahanglang', 'mokuhag', 'importation', 'permit', 'sa', 'National', 'Food', 'Authority', '(', 'NFA', ')', 'sa', 'di', 'pa', 'komprahon', 'ang', 'bugas', 'gikan', 'sa', 'laing', 'nasod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 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] | cebuaner |
6,176 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'opisyales', 'sa', 'BOC', 'Port', 'of', 'Cebu', 'niingon', 'nga', 'posibleng', 'makiha', 'og', 'economic', 'sabotage', 'ang', 'mga', 'smugglers', 'sa', 'bugas', 'tungod', 'sa', 'kadako', 'sa', 'kantidad', 'sa', 'gipayuhot', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,177 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'smuggler', 'nga', 'masakpang', 'naghimo', 'niini', 'mahimong', 'hukman', 'nga', 'mabilanggo', 'sa', 'tibuok', 'kinabuhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,178 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'duha', 'ka', 'alert', 'orders', 'nga', 'may', '25', 'ka', 'container', 'vans', 'siya', 'mismo', 'ang', 'mipirma', ',', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,179 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'kargamento', 'sa', 'bugas', 'niabot', 'sa', 'Sugbo', 'gikan', 'sa', 'China', 'niadtong', 'Nobyembre', '27', ',', '29', 'ug', '30', ',', '2017', 'ug', 'Disyembre', '3', ',', '2017.', 'Gisakay', 'kini', 'sa', 'mga', 'barkong', 'MV', 'Macau', 'Trader', ',', 'MV', 'Kota', 'Jaya', ',', 'MV', 'Lobovia', ',', 'MV', 'AS', 'Ragna', 'ug', 'MV', 'Chattanoga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 8, 8, 0, 7, 8, 0, 7, 8, 8, 0, 7, 8, 0] | cebuaner |
6,180 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'pinayuhot', 'nga', 'bugas', 'imbargohon', 'sa', 'gobyerno', 'ug', 'pagkahuman', 'ibaligya', 'sa', 'BOC', 'Port', 'of', 'Cebu', 'pinaagi', 'sa', 'public', 'auction', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 4, 4, 0, 0, 0, 0, 0] | cebuaner |
6,181 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mokabat', 'sa', '150', 'ka', 'mga', 'bata', 'gihatagan', 'og', 'sayo', 'nga', 'Christmas', 'treat', 'sa', 'pagbukas', 'sa', 'Kasadya', 'sa', 'South', 'Road', 'Properties', '(', 'SRP', ')', 'pinaagi', 'sa', 'paghatag', 'kanila', 'og', 'libreng', 'pagkaon', 'ug', 'rides', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,182 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Emmanuel', 'Ongkiko', ',', 'managing', 'director', 'sa', 'PAPs', ',', 'nipasabot', 'nga', 'gusto', 'nilang', 'lingawon', 'ang', 'mga', 'bata', 'ning', 'Pasko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,183 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dako', 'ang', 'posibilidad', 'nga', 'atol', 'sa', 'Sinulog', 'modagsa', 'ang', 'supply', 'niini', 'sa', 'syudad', 'sa', 'Sugbo', 'tungod', 'sa', 'mga', 'kalingawan', 'ug', 'ang', 'pagpangabot', 'sa', 'mga', 'bisita', 'gikan', 'sa', 'laing', 'lugar', 'sa', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,184 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niini', ',', 'pahugtan', 'pa', 'sa', 'PDEA', 'inabagan', 'sa', 'kapulisan', 'sa', 'Central', 'Visayas', 'ang', 'ilang', 'monitoring', 'kinsa', 'kining', 'mga', 'personalidad', 'nga', 'nalambigit', 'sa', 'pagpamaligya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,185 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'niya', 'nga', 'di', 'sama', 'sa', 'kaulohan', 'ang', 'gidaghanon', 'sa', 'supply', 'sa', 'party', 'drugs', 'nga', 'moabot', 'sa', 'Sugbo', 'tungod', 'sa', 'kamahal', 'niini', 'itandi', 'sa', 'shabu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0] | cebuaner |
6,186 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Makigtagbo', 'sila', 'sa', 'mga', 'bar', 'managers', 'aron', 'tabangan', 'sila', 'pag-ila', 'kinsa', 'kining', 'mga', 'kustomer', 'nga', 'mamaligya', 'sulod', 'sa', 'ilang', 'establisemento', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,187 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Estudyante', 'nalumos', 'human', 'maanod', 'samtang', 'naligo', 'sa', 'sapa', 'sa', 'miaging', 'hapon', 'sa', 'Brgy.', 'Lindogon', ',', 'lungsud', 'sa', 'Sibonga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 5, 0] | cebuaner |
6,188 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'classmate', 'nibutyag', 'nga', 'nisawom', 'sa', 'sapa', 'si', 'Jirmy', ',', 'apan', 'gianod', 'sa', 'kusog', 'nga', 'sulog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,189 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'nasikop', 'mao', 'si', 'kanhi', 'Corporal', 'Zaldy', 'Pareja', ',', '56', ',', 'lumad', 'nga', 'taga', 'Basak', 'Compostela', ',', 'kinsa', 'nakuhaan', 'og', 'usa', 'ka', 'rifle', 'caliber', '.45', ',', 'duha', 'ka', 'kalibre', '.38', 'nga', 'revolver', ',', 'usa', 'ka', '.45', 'caliber', 'pistol', 'ug', 'upat', 'ka', 'gagmay', 'nga', 'mga', 'pakete', 'sa', 'illegal', 'nga', '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, 1, 2, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,190 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Coral', 'nga', 'dunay', 'ilang', 'nadawat', 'nga', 'impormasyon', 'sa', 'nanglabay', 'nga', 'mga', 'semana', 'labot', 'sa', 'giingong', 'kanunay', 'nga', 'pagpabuto', 'sa', 'iyang', 'armas', 'ni', 'Pareja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0] | cebuaner |
6,191 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'ilang', 'gihimong', 'back', 'ground', 'investigation', 'ug', 'surveillance', 'ilang', 'nasuta', 'nga', 'tinuod', 'ang', 'gipang', 'tug-an', 'sa', 'mga', 'residente', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,192 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihimo', 'usab', 'matod', 'pa', 'ang', 'suspek', 'og', 'goon', 'sa', 'usa', 'ka', 'opisyal', 'sa', 'lungsod', ',', 'kinsang', 'ngan', 'gipugngan', 'una', 'samtang', 'wa', 'pa', 'makuhai', 'sa', 'iyang', 'habig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 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 |
6,193 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giangkon', 'hinuon', 'ni', 'Pareja', 'nga', 'sukad', 'niadtong', '2010', 'gihimo', 'siya', 'nga', 'body', 'guard', 'sa', 'opisyal', 'sanglit', 'iya', 'ra', 'usab', 'ning', 'paryente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,194 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'wa', 'niya', 'angkuna', 'nga', 'iya', 'ang', 'drugas', 'nga', 'nakuha', 'matod', 'pa', 'sa', 'iyang', 'posisyon', 'lakip', 'na', 'ang', 'armas', 'nga', 'nakuha', 'usab', 'sa', 'mga', 'sakop', 'sa', 'CIDG', '7', 'sa', 'sulod', 'sa', 'ilang', 'panimay', 'sanglit', 'gibilin', 'lang', 'kini', 'sa', 'wala', 'niya', 'mailhing', '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, 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 |
6,195 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Gibilin', 'ra', 'na', 'sa', 'akoa', ',', 'wala', 'pud', 'ko', 'kaila', 'ato', 'niya', 'basta', 'niingon', 'to', 'siya', 'nga', 'iya', 'daw', 'balikon', 'mao', 'to', 'gihipos', 'ra', 'gyud', 'nako', 'nga', 'gibutang', 'nako', 'sa', 'aparador', ',', 'mao', 'nang', 'pag', 'adto', 'nila', 'naa', 'ra', 'na', 'nila', 'nakuha', 'sa', 'aparador', ',', '”', 'matod', 'ni', 'Pareja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 0] | cebuaner |
6,196 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Na-dismiss', 'siya', 'sa', 'serbisyo', 'niadtong', '1993', 'human', 'gipasanginlan', 'nga', 'mi', 'abandon', 'detachment', 'ug', 'malversation', 'apan', 'iya', 'ning', 'gipanalipdan', 'nga', 'wala', 'siyay', 'sala', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
6,197 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Human', 'niadtong', 'mga', 'panahuna', 'wa', 'na', 'siya', 'kabalik', 'sa', 'serbisyo', 'nga', 'maoy', 'hinungdan', 'nga', 'nag', 'body', 'guard', 'na', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,198 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagtuo', 'si', 'Pareja', 'nga', 'dunay', 'nagpaluyo', 'nga', 'politika', 'sa', 'pagkasikop', 'niya', 'sanglit', 'naa', 'siya', 'sa', 'mga', 'politiko', 'nagtrabaho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
6,199 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Negusyante', 'gidakop', 'human', 'nakuhaag', 'armas', 'dihang', 'gironda', 'sa', 'kapulisan', 'ang', 'iyang', 'balay', 'kagahapon', 'sa', 'alas', '5:45', 'sa', 'buntag', 'sa', 'Brgy.', 'Suba', ',', 'dakbayan', 'sa', 'Danao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0] | cebuaner |
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