Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,500 | 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', 'matod', 'ni', 'Señor', 'nga', 'duna', 'pay', 'daghang', 'mga', 'karaang', 'bomba', 'sa', 'IT', 'park', 'nga', 'makaplagan', 'sa', 'umaabot', 'nga', 'mga', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,501 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'awtoridad', 'nakahupay', 'na', 'gumikan', 'sa', 'kahadlok', 'nga', 'mitumaw', 'nga', 'dala', 'sa', 'vintage', 'bomb', 'ug', 'ipatan-aw', 'sab', 'kini', 'sa', 'mga', 'eksperto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,502 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mihatag', 'sab', 'siya', 'og', 'tambag', 'sa', 'katawhan', 'ilabina', 'sa', 'mga', 'nagtrabaho', 'ug', 'nahaduol', 'sa', 'IT', 'Park', 'nga', 'di', 'sila', 'kinahanglan', 'ma-panic', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0] | cebuaner |
5,503 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'gitataw', 'ni', 'Osmeña', 'nga', 'di', 'lang', 'kini', 'unang', 'higayon', 'nga', 'adunay', 'nakita', 'nga', 'bomba', 'gumikan', 'sa', 'WWII', 'diri', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,504 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'sab', 'ni', 'Osmeña', 'nga', 'wa', 'bisan', 'usa', 'ka', 'kaso', 'mahitungod', 'sa', 'bomba', 'nga', 'nakit-an', 'nga', 'nibuto', 'sukad', 'sa', 'tuig', '1945', 'apan', 'wa', 'kini', 'nagpasabot', 'nga', 'di', 'posibleng', 'mobuto', 'ang', 'bomba', 'nga', 'madiskobre', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,505 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mibutyag', 'sab', 'si', 'Osmeña', 'nga', 'adunay', 'posibilidad', 'nga', 'wala', 'sa', 'saktong', 'kondisyon', 'ang', 'maong', 'bomba', 'kay', 'kung', 'kini', 'aktibo', 'mahimo', 'na', 'kining', 'mobuto', 'kung', 'maigo', 'sa', 'backhoe', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0] | cebuaner |
5,506 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibawo', 'sa', 'Swiss', 'Ambassador', 'to', 'the', 'Philippines', ',', 'Andrea', 'Reichlin', 'nga', 'magsilbi', 'kining', 'unang', 'higayon', 'sa', 'pagsalmot', 'sa', '“Fleur', 'de', 'Passion”', 'sa', 'maong', 'relihiyosong', 'kalihokan', 'sa', 'nasud', 'human', 'sila', 'gidapit', 'sa', 'organizing', 'committee', 'sa', 'pag-apil', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,507 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sama', 'sa', 'ubang', 'sakayan', 'sa', 'dagat', 'nga', 'moapil', 'sa', 'fluvial', 'procession', ',', 'di', 'magpa-iwit', 'ang', '33-metros', 'nga', 'gitas-on', 'sa', 'kanhi', 'German', 'navy', 'vessel', 'nga', 'mobutang', 'og', 'dekorasyon', ',', 'dala', 'ang', 'imahen', 'sa', 'Sr.', 'Sto.', 'Niño', 'nga', 'gihatag', 'ni', 'Lapu-Lapu', 'City', 'Mayor', 'Paz', 'Radaza', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 5, 6, 0, 1, 2, 0] | cebuaner |
5,508 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Suwayan', 'sab', 'nila', 'sa', 'pagsubay', 'sa', 'rota', 'nga', 'gibiyahe', 'sa', 'Portuguese', 'navigator', 'Ferdinand', 'Magellan', 'ubos', 'sa', 'pagdumala', 'sa', 'Geneva-based', 'non-profit', 'organization', 'nga', 'Fondation', 'Pacifique', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
5,509 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'usab', 'kini', 'ang', 'nagdala', 'sa', 'Kristiyanismo', 'dinhi', 'sa', 'nasod', ',', 'labina', 'sa', 'Sugbo', ',', 'nga', 'naghimo', 'sa', 'pag-apil', 'sa', 'mao', 'nga', 'barko', 'nga', 'makahulogonan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,510 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'proyekto', 'ginganlan', 'og', '“The', 'Ocean', 'Mapping', 'Expedition”', 'nga', 'mohimo', 'sab', 'og', 'pagtuon', 'sa', 'sitwasyon', 'sa', 'kadagatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,511 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kinasing-kasing', 'nga', 'mapasalamaton', 'si', 'Mayor', 'Guia', 'Sabanal', 'ngadto', 'kang', 'Mayor', 'Tomas', 'sa', 'hinabang', 'nga', 'ihatag', 'ngadto', 'sa', 'iyang', 'lungsod', 'San', 'Francisco', ',', 'Surigao', 'del', 'Norte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0] | cebuaner |
5,512 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Sabanal', ',', 'kauban', 'ang', 'tulo', 'ka', 'Sangguniang', 'Bayan', '(', 'SB', ')', 'ug', 'laing', 'opisyal', 'nigahin', 'og', 'panahon', 'aron', 'personal', 'nga', 'makapasalamat', 'kang', '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, 1, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,513 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Isip', 'usa', 'ka', '5th', 'class', 'municipality', ',', 'si', 'Sabanal', 'nagkanayon', 'nga', 'dako', 'kaayo', 'kini', 'nga', 'tabang', 'sa', 'lugar', 'ang', 'P2', 'milyones', 'nga', 'hinabang', 'human', 'sila', 'maapektuhi', 'sa', 'linog', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0, 0, 0, 0] | cebuaner |
5,514 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'usab', 'ni', 'Sabanal', 'ang', 'kantidad', 'nga', 'magasto', 'aron', 'hingpit', 'nga', 'maayo', 'gym', 'nga', 'mokabat', 'og', 'P5', 'milyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
5,515 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'di', 'lang', 'P2', 'milyones', 'ang', 'madawat', 'nga', 'hinabang', 'sa', 'katawhan', 'sa', 'Surigao', 'apil', 'na', 'ang', 'paghatag', 'og', 'ambulance', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,516 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahisgotan', 'sab', 'ni', 'Sabanal', 'nga', 'wa', 'silay', 'ambulance', 'ug', 'L300', 'van', 'lang', 'nga', 'sakyanan', 'ang', 'gamiton', 'alang', 'sa', 'pagdala', 'sa', 'mga', 'pasyente', 'padulong', 'sa', 'Davao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 0] | cebuaner |
5,517 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hilabi', 'nga', 'adunay', 'mga', 'pasyente', 'nga', 'di', 'maka-afford', 'para', 'makaadto', 'sa', 'private', 'hospital', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,518 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Davao', 'adunay', 'government', 'hospital', 'diin', 'libre', 'tanan', ',', 'apan', 'kinahanglan', 'pa', 'biyahion', 'sulod', 'sa', 'pito', 'ka', 'oras', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,519 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Upat', 'ka', 'mga', 'pulis', 'sa', 'lungsod', 'sa', 'Cordova', 'gipasanginlan', 'nga', 'illegal', 'nga', 'ningsikop', 'sa', 'usa', 'ka', 'gituohan', 'nga', 'tigamitan', 'sa', 'illegal', 'nga', 'drugas', 'nga', 'way', 'warrant', 'of', 'arrest', 'bisan', 'kon', 'giingong', 'way', 'nakuha', 'nga', 'drugas', 'gikan', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,520 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'hepe', 'sa', 'kapulisan', 'sa', 'mao', 'nga', 'lungsod', 'nanalipod', 'sa', 'iyang', 'mga', 'sakop', ',', 'niingon', 'nga', 'nakuhaan', 'og', 'illegal', 'drugs', 'ang', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,521 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Natingala', 'sila', 'nga', 'sa', 'pag-abot', 'sa', 'police', 'station', ',', 'duna', 'nay', 'usa', 'ka', 'pakete', 'sa', 'illegal', 'drugs', 'nga', 'matod', 'pa', 'nakuha', 'gikan', 'sa', 'iyang', 'bulsa', 'atol', 'sa', 'pagrekisa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,522 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'hugot', 'ning', 'gihimakak', 'sa', 'hepe', 'sa', 'Cordova', 'police', 'station', ',', 'Chief', 'Insp.', 'Clemente', 'Ceralde', ',', 'sanglit', 'nakuhaan', 'nila', 'og', 'drugas', 'si', 'Anoza', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,523 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'ni', 'Ceralde', 'nga', 'una', 'nilang', 'nasikop', 'sa', 'Barangay', 'Alegria', 'ang', 'usa', 'ka', 'gikatahapang', 'drug', 'pusher', 'nga', 'si', 'Hilario', 'Marangga', ',', 'kinsa', 'nakuhaan', 'og', '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, 1, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,524 | 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', 'gisubay', 'ang', 'cellular', 'phone', ',', 'ilang', 'nakita', 'ang', 'binayloay', 'nga', 'mensahe', 'ni', 'Anoza', 'nga', 'ni-order', '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, 1, 0, 0, 0, 0, 0] | cebuaner |
5,525 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'miaging', 'hapon', ',', 'nakadawat', 'og', 'tawag', 'sa', 'telepono', 'ang', 'police', 'station', 'nga', 'dunay', 'kagubot', 'sa', 'Barangay', 'Gabi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,526 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'ilang', 'pag', 'abot', ',', 'si', 'Anoza', 'ang', 'nalambigit', '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] | cebuaner |
5,527 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ila', 'kining', 'girekisa', 'sa', 'pagsiguro', 'nga', 'way', 'dala', 'nga', 'armas', ',', 'apan', 'nakuhaan', 'na', 'hinuon', 'og', 'usa', 'ka', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,528 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'hitabo', 'gi', 'post', 'sa', 'Facebook', 'sa', 'mga', 'higala', 'sa', 'pag-umangkon', 'sa', 'dinakpan', 'nga', 'nabasahan', 'usab', 'sa', 'mga', 'opisyal', 'sa', 'Camp', 'Crame', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0] | cebuaner |
5,529 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'niini', ',', 'ang', 'Police', 'Regional', 'Office', '7', 'nimando', 'og', 'imbestigasyon', 'kon', 'duna', 'bay', 'nalapas', 'sa', 'police', 'operational', 'procedure', 'ang', 'tulo', 'ka', 'mga', 'pulis', 'nga', 'gipasanginlan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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] | cebuaner |
5,530 | 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', 'kabanay', 'ni', 'Anoza', 'niangkon', 'sa', 'Superbalita', 'Cebu', 'nga', 'tigamitan', 'sa', 'illegal', 'nga', 'drugas', 'ang', 'dinakpan', 'apan', 'giingong', 'niundang', 'na', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,531 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Andam', 'hinuon', 'nga', 'atubangon', 'sa', 'hepe', 'sa', 'Cordova', 'police', 'station', 'ang', 'imbestigasyon', 'nga', 'himuon', 'sa', 'PRO', '7', 'ug', 'ilang', 'gibarugan', 'nga', 'wa', 'silay', 'nalapas', 'nga', 'operational', 'procedure', 'sa', 'PNP', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
5,532 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Posible', 'pa', 'hinuon', 'kining', 'mapun-an', 'tungod', 'kay', 'nagpadayon', 'pa', 'man', 'ang', 'assessment', 'sa', 'kadaot', 'nga', 'gibilin', 'sa', 'bagyong', 'Urduja', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
5,533 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'na', 'sa', 'P123.8', 'milyones', 'ang', 'inisyal', 'nga', 'kantidad', 'sa', 'danyos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,534 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'niini', 'ang', 'P118', 'million', 'nga', 'kadaot', 'sa', 'imprastrktura', 'nunot', 'sa', 'ni-collapse', 'nga', 'dalan', 'sa', 'brgy.', 'Ilihan', 'ug', 'brgy.', 'Kal-anan', 'sa', 'Tabogon', 'ug', 'brgy.', 'Panugnawan', 'ug', 'Tinubdan', 'sa', 'lungsod', 'sa', 'Medellin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 0, 5, 6, 0, 5, 0, 0, 0, 5, 0] | cebuaner |
5,535 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'San', 'Remegio', 'maoy', 'nitala', 'sa', 'inisyal', 'nga', 'labing', 'dako', 'nga', 'kadaot', 'nga', 'moabot', 'sa', 'dul-an', 'P5', 'milyones', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,536 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nalipay', 'siya', 'nga', 'bisan', 'sa', 'kadaot', 'nga', 'naangkon', 'sa', 'pag-igo', 'sa', 'bagyo', ',', 'way', 'natala', 'nga', 'casualty', 'sa', 'iyang', 'lungsod', 'ug', 'mga', 'silingan', 'niining', 'dapit.', '(', 'AZDL', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,537 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Disyembre', '13', ',', 'gipadad-an', 'na', 'og', '‘end', 'of', 'contract', 'notice’', 'si', 'Cabiging', 'gikan', 'sa', 'buhatan', 'sa', 'CSWS', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 0] | cebuaner |
5,538 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'sa', 'unang', 'semana', 'sa', 'Enero', 'ipatuman', 'ni', 'Perez', 'ang', 're-organization', 'ug', 'ang', 'pagpa-ubos', 'pag-usab', 'sa', 'training', 'sa', 'tanang', 'staff', 'sa', 'Home', 'Children', 'Center', 'alang', 'sa', 'proper', 'handling', 'sa', 'mga', 'bata', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,539 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'gitutokan', 'sa', 'opisyal', 'ang', 're-evaluation', 'sa', 'mga', 'kawani', 'sa', 'ilang', 'performance', 'sa', 'pagdumala', 'sa', 'facility', 'aron', 'masuta', 'kinsay', 'ipabilin', 'o', 'ibalhin', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,540 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Milanat', 'og', 'dul-an', 'sa', '12', 'ka', 'oras', 'nga', 'niali', 'sa', 'lane', 'paingon', 'sa', 'norte', 'ang', 'Fuso', 'truck', 'nga', 'nagdala', 'sa', 'plate', 'number', 'YKY', '–', '306', 'sanglit', 'di', 'madali', 'sa', 'pagguyod', 'gumikan', 'sa', '20', 'tonilada', 'nga', 'gibug-aton', 'sa', 'karga', 'nga', 'construction', 'materials', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,541 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagsugod', 'kahuot', 'ang', 'trapiko', 'sa', 'dapit', 'sukad', 'sa', 'alas', '8:24', 'niadtong', 'Lunes', 'sa', 'gabii', 'ngadto', 'na', 'sa', 'alas', '7:38', 'kagahapon', 'sa', 'buntag', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,542 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paingon', 'sa', 'norte', 'sa', 'Sugbo', 'ang', 'maong', 'sakyanan', 'nga', 'gikan', 'pa', 'sa', 'lalawigan', 'sa', 'Bohol', 'sa', 'dihang', 'nahitabo', 'ang', 'aksidente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
5,543 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Louie', 'Alivio', 'sa', 'Team', ',', 'nitug-an', 'nga', 'gamit', 'ang', 'forklift', 'napadaplin', 'ang', 'naaberiya', 'nga', 'sakyanan', 'ug', 'giayo', 'ra', 'usab', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,544 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'motorista', 'nagbagulbol', 'sa', 'nag-aginod', 'nga', 'trapiko', 'sa', 'maong', 'dapit', 'sa', 'dihang', 'nag-ulhos', 'ulhos', 'na', 'ang', 'mga', 'sakyanan', 'nga', 'gipa-agi', 'sa', 'maong', 'kalsada', 'ug', 'kadtong', 'gikan', 'sa', 'norte', 'gihatagan', 'og', 'higayon', 'nga', 'mo-counter', 'flow', 'aron', 'lang', 'makalahos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,545 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Padayon', 'pa', 'nga', 'gisusi', 'sa', 'City', 'Agriculture', 'Office', 'ang', 'kalapad', 'sa', 'kadaot', 'nga', 'nadala', 'sa', 'bagyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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] | cebuaner |
5,546 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lakip', 'sa', 'apektado', 'nga', 'mga', 'barangay', 'ang', 'Adlaon', ',', 'Taptap', ',', 'Sudlon', '1', ',', 'Sudlon', '2', ',', 'Sinsin', ',', 'Bonbon', ',', 'Pungol', 'Sibugay', ',', 'Sirao', ',', 'Guba', ',', 'Lusaran', ',', 'Malubog', 'ug', '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, 5, 0, 5, 0, 5, 6, 0, 5, 6, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0] | cebuaner |
5,547 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'na', 'unya', 'ni', 'ang', 'gamiton', 'sa', 'matag', 'Sugbuanon', 'sa', 'dakbayan', 'sa', 'Sugbo', 'kon', 'hingpit', 'na', 'nga', 'mapatuman', 'ang', '911', 'system', 'sunod', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,548 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', ',', 'gipresentar', 'sa', 'PLDT', 'ngadto', 'kang', 'Cebu', 'City', 'Mayor', 'Tomas', 'Osmeña', 'ang', 'systema', 'sa', '911', 'aron', 'magamit', 'kini', 'isip', 'sentralisado', 'na', 'nga', 'emergency', 'number', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,549 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'gipresentar', 'sa', 'mga', 'opisyales', 'sa', 'PLDT', 'ang', 'Smart', 'SOS', 'Dispatch', ',', 'diin', 'usa', 'kini', 'ka', 'digital', 'emergency', 'response', 'mechanism', 'nga', 'maseguro', 'ang', 'kaepektibo', 'sa', 'sistema', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,550 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mahitabo', 'kon', 'ang', 'caller', 'motawag', '911', 'usa', 'kini', 'ka', 'dispatch', 'system', 'nga', 'makita', 'sa', 'command', 'center', 'ug', 'mahibaw-an', 'kon', 'asa', 'ang', 'mga', 'responder', 'pinaagi', 'sa', 'GPS', 'tracking', 'ug', 'mahibaw-an', 'kon', 'pila', 'ka', 'minutos', 'maabot', 'ang', 'responders', 'ngadto', 'sa', 'tawo', 'nga', 'mi-report', 'sa', 'insidente', 'sa', '911', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,551 | 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', '911', 'gipatuman', 'na', 'usab', 'sa', 'ubang', 'lugar', 'sama', 'sa', 'Ilocos', ',', 'Puerto', 'Princesa', ',', 'Cavite', ',', 'La', 'Union', 'ug', 'daghang', 'local', 'government', 'units', 'ang', 'mikuha', 'sa', 'ilang', '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, 5, 0, 5, 6, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,552 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Di', 'mominus', '45', 'ka', 'mga', 'Smart', 'SOS', 'Dispatch', 'service', 'units', 'ang', 'gamiton', 'sa', 'sistema', 'ug', 'laing', '70', 'ka', 'mga', 'CCTV', 'cameras', 'ang', 'itaod', '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, 0, 0] | cebuaner |
5,553 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasuta', 'nga', 'ang', 'City', 'government', 'mogasto', 'og', 'P15', 'milyon', 'alang', 'sa', 'operasyon', 'niini', 'sunod', 'tuig', 'sanglit', 'naa', 'naman', 'kini', 'sa', 'annual', 'budget', 'alang', 'sa', '2018', 'ubos', 'sa', 'disaster', 'funds', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,554 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Bañacia', 'nga', 'ang', 'gibayran', 'sa', 'siyudad', 'mao', 'ang', 'sistema', 'samtang', 'ang', 'mga', 'responder', 'gikan', 'sa', 'City', 'hall', 'nga', 'ipaubos', 'sa', 'pagbansay-bansay', 'aron', 'motubag', 'og', 'mga', 'emerhensiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
5,555 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'usab', 'ni', 'Osmeña', 'nga', 'itandi', 'sa', 'kasamatgan', 'nga', 'CCTVs', 'sa', 'Sugbo', 'matod', 'pa', 'mas', 'advantage', 'kini', 'sa', 'siyudad', 'tungod', 'kay', 'di', 'na', 'magproblema', 'pa', 'ang', 'city', 'government', 'sa', 'pag-mintenar', 'niini', 'tungod', 'kay', 'ang', 'PLDT', 'naman', 'ang', 'mohimo', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0] | cebuaner |
5,556 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Liboan', 'na', 'ka', 'mga', 'anti-dengue', 'vaccine', 'nga', 'Dengvaxia', 'ang', 'nabawi', 'sa', 'Department', 'of', 'Health', '7', '(', 'DOH', ')', 'gikan', 'sa', 'pipila', 'ka', 'mga', 'baranggay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,557 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Padayun', 'nga', 'pag-monitor', 'ug', 'pag-alima', 'sa', 'mga', 'bata', 'nga', 'nabakunahan', 'ang', 'ilang', 'gihatagan', 'og', 'importansya', 'sapagka', 'karon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,558 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitataw', 'sab', 'niya', 'nga', 'ang', 'pagpangusog', 'sa', 'kampanya', 'batok', 'sa', 'dengue', 'ang', 'mas', 'labing', 'gihatagan', 'og', 'tumong', 'sa', 'ilahang', 'departamento', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,559 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aduna', 'nay', 'labaw', 'sa', '3,741', 'ka', 'mga', 'garapa', 'sa', 'mga', 'vaccine', 'ang', 'nabalik', 'ngadto', 'sa', 'DOH', 'duha', 'na', 'ka', 'semana', 'ang', 'milabay', 'apan', 'wala', 'pa', 'sila’y', 'nadawat', 'nga', 'mga', 'plano', 'mahitungod', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,560 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'DOH', '7', 'director', 'Jaime', 'Bernadas', 'nagkanayon', 'nga', 'kaniadto', 'nga', 'nakadawat', 'siya', 'og', 'mando', 'gikan', 'ni', 'Health', 'Secretary', 'Francisco', 'Duque', 'nga', 'isuspenso', 'ang', 'paghatag', 'og', 'mga', 'anti-dengue', 'vaccine', 'nga', 'Dengvaxia', 'ug', 'bawion', 'kini', 'sa', 'labing', 'madali', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,561 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Bernadas', 'nga', 'ang', 'tanan', 'nga', 'mga', 'bakuna', 'batok', 'sa', 'dengue', 'mahimong', 'ipadala', 'ngadto', 'sa', 'DOH', 'Central', 'Office', 'sa', 'Manila', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 0, 5, 0] | cebuaner |
5,562 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'tungod', 'nakadawat', 'sila', 'og', 'daghan', 'nga', 'mga', 'taho', 'nga', 'daghang', 'mga', 'bata', 'ang', 'grabeng', 'na-dengue', 'human', 'sa', 'pagbakuna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,563 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'sab', 'niya', 'ang', 'publiko', 'sa', 'paglimpyo', 'kanunay', 'sa', 'ilang', 'palibot', 'aron', 'malikayan', 'ang', 'pagdaghan', 'sa', 'mga', 'lamok', 'nga', 'posibleng', 'makadala', 'og', 'sakit', 'nga', 'dengue', 'kay', 'ang', 'tinuod', 'nga', 'solusyon', 'sa', 'maong', 'sakit', 'mao', 'ang', 'kalimpyo', 'ug', 'kooperasyon', 'sa', 'katawhan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,564 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawng', 'Biyernes', 'gipaabot', 'nga', 'modagsa', 'ang', 'daghang', 'pasahero', 'sa', 'Cebu', 'South', 'Bus', 'Terminal', '(', 'CSBT', ')', 'hangtod', 'na', 'sa', 'petsa', '24', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0] | cebuaner |
5,565 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugdan', 'na', 'ang', 'pagpakatap', 'og', 'dugang', 'gwardiya', 'ug', 'pulis', 'aron', 'pagseguro', 'sa', 'kahapsay', 'ug', 'kalinaw', 'sa', 'pasilidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,566 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '“open-bag', 'policy”', 'hugot', 'nga', 'ipatuman', 'ug', 'bisan', 'ang', 'mga', 'selyado', 'nga', 'karton', 'paablihan', 'aron', 'masusi', 'sa', 'pagsud', 'sa', 'terminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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 |
5,567 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karon', 'pa', 'lang', ',', 'nagsugod', 'na', 'sa', 'pagsaka', 'ang', 'ihap', 'sa', 'mga', 'pasahero', 'sa', 'CSBT', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,568 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Biyernes', 'ingon', 'man', 'sa', 'Sabado', 'ug', 'Dominggo', ',', 'nga', 'bisperas', 'sa', 'Pasko', ',', 'gipaabot', 'ni', 'CSBT', 'operations', 'manager', 'Joey', 'Herrera', 'nga', 'mas', 'modaghan', 'pa', 'kini', 'ug', 'posibleng', 'magdasok', 'na', 'sa', 'terminal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 3, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,569 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sagad', 'niini', 'mao', 'kadtong', 'mga', 'mohimo', 'og', 'last', 'minute', 'shopping', 'o', 'duna', 'pay', 'tambungan', 'nga', 'mga', 'Christmas', 'party', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,570 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Subay', 'niini', ',', 'sugdan', 'na', 'karong', 'Biyernes', 'ang', 'pagpakatap', 'og', 'dugang', 'security', 'ug', 'police', 'personnel', 'sa', 'pasilidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,571 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Herrera', ',', 'nga', 'sama', 'sa', 'niaging', 'mga', 'tingsakay', ',', 'dunay', 'mga', 'kawani', 'sa', 'Kapitolyo', 'ang', 'i-augment', 'usab', 'aron', 'makatabang', 'sa', 'crowd', 'control', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,572 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'labing', 'dako', 'nga', 'organisasyon', 'sa', 'merkado', 'sa', 'Tabunok', 'mibutyag', 'usab', 'nga', 'aduna', 'karoy', 'mga', 'lakang', 'ang', 'market', 'committee', 'pagbuak', 'sa', 'ilang', 'grupo', 'pinaagi', 'sa', 'paghimog', 'lain-laing', 'grupo', 'sa', 'matag', 'section', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,573 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'si', 'City', 'Administrator', 'Rudelyn', 'Navarro', 'mihikakak', 'sa', 'pasangil', 'ug', 'miawhag', 'sa', 'mga', 'mireklamo', 'sa', 'pag-adto', 'sa', 'iyang', 'buhatan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,574 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kahinumdoman', ',', 'atol', 'sa', 'gihimong', 'groundbreaking', 'ceremony', 'sa', 'pagsugod', 'nagtukod', 'sa', 'modernong', 'merkado', 'sa', 'Tabunok', 'niadtong', 'Septiyembre', 'sa', 'miaging', 'tuig', 'dungan', 'nga', 'nanumpa', 'usab', 'sa', 'ilang', 'katungdanan', 'ang', 'mga', 'opisyal', 'sa', 'Tabunok', 'Fish', 'Vendors', 'Association', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0] | cebuaner |
5,575 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kining', 'maong', 'mga', 'opisyal', 'ug', 'ilang', 'mg', 'miyembro', 'maoy', 'sentro', 'karon', 'sa', 'reklamo', 'human', 'giingong', 'naneguro', 'sa', 'puwesto', 'bisan', 'diha', 'palang', 'sa', 'ilang', 'temporaryong', 'gibalhinan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,576 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'gibutyag', 'nga', 'tanang', 'mga', 'opisyal', 'ilang', 'paryente', ',', 'higala', 'nga', 'manidahay', 'gibutyag', 'sa', 'atubangan', 'nga', 'bahin', 'samtang', 'sila', 'ubang', 'mga', 'manindahay', 'nga', 'di', 'sakop', 'sa', 'ilang', 'organisasyon', 'gibutang', 'sa', 'layo', 'na', 'nga', 'dait', 'nga', 'di', 'na', 'hapit', 'maabot', 'ang', 'mga', 'mamalitay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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] | cebuaner |
5,577 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'matod', 'ni', 'Lasquite', 'nga', 'di', 'sila', 'gusto', 'nga', 'sa', 'higayong', 'mamalhin', 'na', 'sila', 'sa', 'ilang', 'puwesto', 'sa', 'bag-ong', 'merkado', 'mao', 'gihapon', 'kini', 'ang', 'mahitabo', 'nga', 'adunay', 'pagpabor-pabor', 'ang', 'market', 'committee', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,578 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gani', 'nakadawat', 'silag', 'report', 'nga', 'mismo', 'ang', 'maong', 'grupo', 'nag-away', 'na', 'tungod', 'kay', 'wa', 'magkasinabot', 'sa', 'ilang', 'puwesto', 'kon', 'asa', 'sila', 'mahimutang', 'kon', 'mahuman', 'na', 'ang', 'merkado', 'sa', 'Tabunok.', 'Gumikan', 'niini', 'nanawagan', 'sila', 'ni', 'Gullas', 'ug', 'ni', 'Kong.', 'Samsam', 'Gullas', 'nga', 'mopatunga', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 2, 0, 0, 0, 0] | cebuaner |
5,579 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipirmahan', 'na', 'ni', 'Presidente', 'Rodrigo', 'Duterte', 'isip', 'balaud', 'ang', 'tax', 'reform', 'package', 'ug', 'ang', '2018', 'annual', 'budget', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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] | cebuaner |
5,580 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpasabot', 'nga', 'kadtong', 'nag-income', 'og', 'P21,000', 'matag', 'buwan', 'paubos', ',', 'di', 'na', 'mobayad', 'og', 'buhis', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,581 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'aron', 'matapakan', 'ang', 'kakuwang', 'sa', 'kita', 'gikan', 'sa', 'income', 'taxes', ',', 'ang', 'mga', 'Pinoy', 'mobayad', 'og', 'mas', 'mahal', 'nga', 'excise', 'tax', 'sa', 'fuel', ',', 'sakyanan', ',', 'sigarilyo', 'ug', 'mga', 'ilimnon', 'nga', 'tam-is', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,582 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', '2018', 'General', 'Appropriations', 'Act', 'naggahin', 'og', 'P3.7', 'trillion', 'alang', 'sa', 'operations', 'sa', 'kagamhanan', 'gikan', 'sa', 'Enero', '1', 'ngadto', 'sa', 'Disyembre', '31', ',', '2018', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,583 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giingong', 'manghilot', 'ang', 'suspek', 'sa', 'iyang', 'mga', 'bata', ',', 'kinsa', 'adunay', 'gipangmu', 'sa', 'lawas', 'atol', 'sa', 'managlahing', 'mga', 'hitabo', 'niadtong', 'milabay', 'nga', '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] | cebuaner |
5,584 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'mga', 'nabiktima', ',', 'unom', 'niini', 'ang', 'mga', 'menor', 'de', 'edad', 'diin', 'ang', 'labing', 'bata', 'nag-edad', 'og', '11', 'anyos', 'samtang', 'usa', 'niini', 'ang', 'bag-o', 'lang', 'nag-18', 'anyos', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,585 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'sa', 'hapon', ',', 'pormal', 'na', 'nga', 'gipasaka', 'ang', 'tulo', 'ka', 'mga', 'kaso', 'batok', 'sa', 'social', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,586 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Perez', 'nagkanayon', 'nga', 'nasakpan', 'si', 'Cabiging', 'sa', 'iyang', 'binuhatan', 'sa', 'dihang', 'usa', 'sa', 'mga', 'bata', 'ang', 'nisumbong', 'ngadto', 'sa', 'nagdumala', 'sa', 'pasilidad', 'kalabot', 'sa', 'pagpangabuso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,587 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'niya', 'nga', 'sa', 'pagpanghilot', 'ni', 'Cabiging', ',', 'manghikap', 'siya', 'sa', 'pribadong', 'parte', 'sa', 'lawas', 'sa', 'mga', 'bata', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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] | cebuaner |
5,588 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'insidente', 'nahitabo', 'niadtong', 'Nobiyembre', '16', 'sa', 'udto', 'diin', 'si', 'Liam', ',', 'kinsa', 'bag-o', 'lang', 'nagsaulog', 'sa', 'iyang', 'pagka-18', 'anyos', ',', 'gihilot', 'sa', 'iyang', '“Tita', 'Marlon”', 'human', 'nisakit', 'iyang', 'ulo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 0, 0, 0, 0, 0] | cebuaner |
5,589 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hapit', 'masumbagi', 'ni', 'Liam', 'si', 'Cabiging', 'human', 'manghikap', 'sa', 'iyang', 'kinatawo', ',', 'maayo', 'na', 'lang', 'kay', 'giuwang', 'siya', 'sa', 'gwardiya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,590 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'hitabo', ',', 'gitug-an', 'ni', 'Liam', 'ang', 'insidente', 'ngadto', 'sa', 'tagdumala', 'sa', 'pasilidad', 'nga', 'si', 'Maricel', 'Yu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 0] | cebuaner |
5,591 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitug-an', 'usab', 'ang', 'biktima', 'nga', 'kuyog', 'sa', 'iyang', 'kauban', 'sa', 'center', ',', 'gipatan-aw', 'sila', 'og', 'law-ay', 'nga', 'salida', 'sa', 'cellphone', 'ni', 'Cabiging', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 0] | cebuaner |
5,592 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pagka-hapon', 'sa', 'samang', 'adlaw', ',', 'ang', '11', 'anyos', 'nga', 'si', 'Mar', 'nagpahilot', 'na', 'sab', 'ug', 'gihikap', 'ni', 'Cabiging', 'sud', 'sa', 'classroom', 'human', 'naglain', 'ang', 'lawas', 'niini', 'kay', 'gihilantan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,593 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pagka-ugma', ',', 'si', 'Luis', 'ang', 'nabiktima', 'na', 'usab', 'ni', 'Cabiging', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 1, 0] | cebuaner |
5,594 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'alas', '10', 'niadtong', 'Nobiyembre', '19', ',', 'ang', '13', 'anyos', 'nga', 'si', 'Richard', 'ang', 'giingon', 'nga', 'gipanamastamasan', 'sa', 'suspek', 'sud', 'sa', 'ilang', 'library', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,595 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'pa', 'ni', 'Richard', 'kinsa', 'gi-uyab', 'sa', 'suspek', 'nga', 'gigunitan', 'iyang', 'kamot', 'sa', 'library', 'ug', 'sila', 'nagka-uyab', 'tungod', 'kay', 'namugos', 'niya', 'si', 'Cabiging', 'ug', 'di', 'buhian', 'niini', 'ang', 'kamot', 'kon', 'di', 'siya', 'mosugot', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,596 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'wa', 'pa', 'usab', 'nahitabo', 'ang', 'insidente', 'anang', 'adlawa', ',', 'si', 'Richard', 'niingon', 'nga', 'gipasaka', 'sila', 'sa', 'suspek', 'sa', 'second', 'floor', 'sa', 'pasilidad', 'sa', 'iyang', 'mga', 'kauban', 'ug', 'gipasud', 'tagsa-tagsa', ',', 'sa', 'iyang', 'pagsulod', ',', 'gihikap', 'matod', 'pa', 'iyang', 'kinatawo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,597 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sumala', 'ni', 'Perez', 'nga', 'human', 'nila', 'mahinabi', 'ang', 'upat', ',', 'wa', 'na', 'nila', 'pangutan-a', 'sa', 'hitabo', 'ang', '14', 'anyos', 'nga', 'si', 'Jon', 'ug', '12', 'anyos', 'nga', 'si', 'Chris', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,598 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahibaw-an', 'ni', 'Perez', 'ang', 'pagpangabuso', 'niadtong', 'Nobiyembre', '21', 'ug', 'nianang', 'adlawa', ',', 'gi-pull-out', 'sa', 'trabaho', 'si', 'Cabiging', 'ug', 'niumol', 'og', 'investigation', 'team', 'ang', 'CSWS', 'kalabot', 'sa', 'mga', 'insidente', 'sa', 'pagpangabuso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,599 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'Disyembre', '11', ',', 'ang', 'mga', 'social', 'worker', 'gikan', 'sa', 'DSWD', ',', 'mga', 'psychologist', ',', 'taga', 'city', 'legal', 'office', 'ug', 'si', 'Perez', 'personal', 'nga', 'nihinabi', 'sa', 'mga', 'bata', 'kinsa', 'nisaysay', 'sa', 'ilang', 'nahi-aguman', 'gikan', 'ni', 'Cabiging', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
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