Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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6,300 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', '24', 'ka', 'LGUs', 'sa', 'amihanan', 'ug', 'habagatang', 'bahin', 'sa', 'lalawigan', 'sa', 'Sugbo', 'nga', 'on-going', 'ang', 'ilang', 'pagpahigayon', 'sa', 'CBTP.', '(', 'Michelle', 'Anne', 'Obor', ',', 'USJR', 'intern', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 7, 0, 1, 2, 2, 0, 3, 0, 0] | cebuaner |
6,301 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'sa', '150', 'metros', 'ang', 'gilay-on', 'sa', 'kasundalohan', 'gikan', 'sa', 'mga', 'NPA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0] | cebuaner |
6,302 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'usab', 'masayri', 'sa', 'bahin', 'sa', 'mga', 'NPA', 'kinsa', 'miatras', 'human', 'sa', 'pipila', 'ka', 'minutos', 'nga', 'pinusilay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,303 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihimo', 'gilayon', 'ang', 'hot', 'pursuit', 'operation', 'batok', 'sa', 'NPA', 'ug', 'gialerto', 'ang', 'uban', 'nga', 'kampo', 'sa', 'militar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,304 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sud', 'ning', 'tuiga', ',', 'makadaghan', 'nga', 'higayon', 'na', 'nag-engkwentro', 'ang', 'NPA', 'ug', 'militar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
6,305 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Lima', 'ka', 'kanhi', 'sakop', 'sa', 'New', 'Peoples', 'Army', '(', 'NPA', ')', 'ang', 'nakadawat', 'og', 'tabang', 'gikan', 'sa', 'gobyerno', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,306 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dul-an', 'sa', 'P100,000', 'ang', 'nadawat', 'sa', 'mga', 'kanhi', 'NPA', 'diin', 'lakip', 'na', 'niini', 'ang', 'cash', 'ug', 'livelihood', 'assistance', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,307 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Gob.', 'Dominic', 'Petilla', ',', 'di', 'sila', 'mohunong', 'nga', 'makab-ot', 'ang', 'kalinaw', 'sa', 'lugar', 'ug', 'nagpasalamat', 'sa', 'mga', 'mitahan', 'nga', 'kanhi', 'NPA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
6,308 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipalanog', 'usab', 'ni', 'Brigadier', 'General', 'Francisco', 'Mendoza', 'Jr.', ',', 'labaw', 'sa', '802', 'brigade', 'sa', 'Philippine', 'Army', ',', 'ang', 'ihap', 'sa', 'mitahan', 'nga', 'NPA', 'naghatag', 'og', 'maayong', 'indikasyon', 'sa', 'kahapsay', 'ug', 'kalinaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 3, 4, 0, 3, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,309 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samdan', 'nga', 'gidala', 'sa', 'tambalanan', 'ang', 'usa', 'ka', 'panday', 'human', 'giingong', 'gitigbas', 'sa', 'iyang', 'higala', 'dihang', 'miinit', 'ang', 'panaglalis', 'samtang', 'nagtagay', 'didto', 'sa', 'Barangay', 'Lico-lico', ',', 'lungsod', 'sa', 'Sevilla', 'niadtong', 'Martes', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
6,310 | 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', 'Arsenio', 'Paler', ',', 'minyo', 'ug', 'residente', 'sa', 'Baranagy', 'Bayawan', ',', 'Sevilla', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 0] | cebuaner |
6,311 | 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', 'giila', 'nga', 'si', 'Menardo', 'Orillo', ',', 'minyo', 'ug', 'residente', 'sa', 'Brgy', 'Lico-lico', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 5, 6, 0] | cebuaner |
6,312 | 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', ',', 'nagtagay', 'ang', 'grupo', 'kalit', 'lang', 'nagkalalis', 'ang', 'biktima', 'ug', 'suspek', 'diin', 'nagsinukliay', 'og', 'mga', 'mahait', 'nga', 'pulong', 'nga', 'maoy', 'nakapainit', 'sa', 'panagbangi', 'sa', 'duha', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,313 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ang', 'probinsiya', 'sa', 'Bohol', 'nga', 'napili', 'nga', 'bisitahon', 'sa', 'mga', 'kandidata', 'sa', 'Miss', 'Universe', '2017', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0] | cebuaner |
6,314 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'gituohan', 'nga', 'ang', 'bana', 'ang', 'naghimo', 'sa', 'krimen', 'sanglit', 'nakit-an', 'sila', 'sa', 'ilang', 'mga', 'silingan', 'nga', 'nagtinubagay', 'ug', 'gituohan', 'nga', 'nakainom', 'usab', 'ni', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,315 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'biktima', 'giila', 'nga', 'silang', 'Liesyl', 'Bordadora', ',', '40', ',', 'negosyante', 'og', 'pagkaon', 'sa', 'maong', 'dapit', ',', 'ug', 'ang', 'bana', 'nga', 'si', 'Edwin', 'Bordadora', ',', '37', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0] | cebuaner |
6,316 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'nakadiskobre', 'sa', 'asawa', 'ang', 'kapuyo', 'sa', 'anak', 'niini', 'nga', 'niagi', 'sa', 'tindahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,317 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nabantayan', 'niini', 'nga', 'nagkagubot', 'ang', 'mga', 'butang', 'sa', 'sulod', 'sa', 'tindahan', 'nga', 'maoy', 'hinungdan', 'nga', 'nagduda', 'ni', 'nga', 'dunay', 'dautang', 'nahitabo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,318 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakita', 'niya', 'ang', 'pat-ak', 'sa', 'dugo', 'sa', 'dalan', 'nga', 'maoy', 'iyang', 'gisunod', 'ug', '10', 'ngadto', 'sa', '15', 'metros', 'duol', 'sa', 'bung-aw', ',', 'nag', 'buy-od', 'ang', 'lawas', 'ni', 'Liesyl', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 0] | cebuaner |
6,319 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gituohan', 'nga', 'pasado', 'alas', 'siete', 'pa', 'sa', 'gabii', 'nahitabo', 'ang', 'krimen', 'sanglit', 'usa', 'sa', 'mga', 'silingan', 'nakabati', 'pa', 'og', 'singgit', 'nga', 'nangayo', 'og', 'panabang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,320 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Sa', 'pagkakaron', 'wala', 'pa', 'mi', 'suspek', 'kay', 'ang', 'unang', 'gidudahan', 'nila', 'apil', 'gani', 'ang', 'anak', 'nga', 'ang', 'ilang', 'papa', 'ang', 'nagpatay', 'sa', 'ilang', 'mama', 'pero', 'pagkakita', 'namo', 'nga', 'namatay', 'sad', 'ang', 'bana', 'dako', 'ang', 'posibilidad', 'nga', 'naay', 'laing', 'nagpatay', 'nila', ',', '”', 'matod', 'ni', 'SPO2', 'Winston', 'Ybañez', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 2, 0] | cebuaner |
6,321 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Ybañez', 'nga', 'dunay', 'kasuko', 'ang', 'killer', 'sa', 'magtiayon', 'tungod', 'sa', 'kadaghan', 'sa', 'mga', 'samad', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,322 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'bukton', 'ni', 'Edwin', 'hapit', 'maputol', 'ug', 'naboak', 'na', 'ang', 'bagul-bagul', 'niini', 'samtang', 'si', 'Liesyl', 'dunay', 'mga', 'tinigbasan', 'sa', 'lawas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | cebuaner |
6,323 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagtuo', 'ang', 'mga', 'imbestigador', 'nga', 'giatangan', 'ang', 'magtiayon', 'base', 'sa', 'nakitang', 'tinigbasan', 'sa', 'mga', 'sagbot', 'sa', 'dapit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,324 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakakuha', 'ang', 'Crime', 'Scene', 'Investigation', 'Unit', 'og', 'kwarta', 'nga', 'iya', 'ni', 'Liesyl', 'lakip', 'na', 'ang', 'mga', 'gipangompra', 'niini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,325 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mopalawom', 'pa', 'sa', 'imbestigasyon', 'ang', 'kapulisan', 'aron', 'motibo', 'masayran', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,326 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'ka', 'bag-ong', 'units', 'nga', 'naggamit', 'sa', 'moderno', 'nga', 'teknolohiya', 'nga', 'daling', 'makabantay', 'sa', 'mga', 'kalapasan', 'sa', 'trapiko', 'maoy', 'gamiton', 'unya', 'sa', 'Traffic', 'Enforcement', 'Agency', 'of', 'Mandaue', '(', 'TEAM', ')', 'sa', 'pagdakop', 'sa', 'mga', 'nilapas', 'sa', 'lagda', 'sa', 'trapiko', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,327 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Glenn', 'Antigua', ',', 'pangulo', 'sa', 'Team', ',', 'gisugdan', 'na', 'nila', 'ang', 'paggamit', 'sa', 'Intelligent', 'Transport', 'System', '(', 'ITS', ')', 'sayo', 'ning', 'semanaha', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0] | cebuaner |
6,328 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'usa', 'ka', 'unit', 'sa', 'camera', 'niini', 'gitaod', 'sa', 'may', 'Maguikay', 'flyover', 'niadtong', 'Martes', 'samtang', 'ang', 'laing', 'usa', 'ka', 'unit', 'anaa', 'gitaod', 'sa', 'sakyanan', 'sa', 'Team', 'ug', 'gisuwayan', 'na', 'nila', 'kini', 'didto', 'sa', 'City', 'Times', 'Square', 'sa', 'miaging', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0] | cebuaner |
6,329 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pakighinabi', 'sa', 'Superbalita', 'Cebu', ',', 'si', 'Antigua', 'niingon', 'nga', 'ang', 'camera', 'sa', 'maong', 'sistema', 'makabasa', 'sa', 'plate', 'number', 'sa', 'mga', 'sakyanan', ',', 'speed', 'limit', '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, 3, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,330 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sab', 'ni', 'Mayor', 'Luigi', 'Quisumbing', 'nga', 'nga', 'adunay', 'kapabilidad', 'nga', 'morehistro', 'ang', 'maong', 'sistema', 'og', 'kalapasan', 'susama', 'sa', 'overspeeding', 'ug', 'illegal', 'parking', ',', 'counterflowing', ',', 'illegal', 'turns', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,331 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'hulagway', 'nga', 'makuha', 'niini', 'ilabi', 'na', 'sa', 'plate', 'number', 'diretsong', 'ipadala', 'sa', 'Land', 'Transportation', 'Office', '(', 'LTO', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0] | cebuaner |
6,332 | 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', 'Antigua', 'nga', 'dako', 'ang', 'matabang', 'sa', 'kahimanan', 'ilabi', 'na', 'sa', 'pagsulbad', 'sa', 'mga', 'krimen', 'susama', 'sa', 'pagpangawat', 'og', 'sakyanan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,333 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'sistema', 'nga', 'gitaod', 'sa', 'ilang', 'sakyanan', 'ilabi', 'na', 'sa', 'mga', 'dagkong', 'kalihukan', 'alang', 'sa', 'seguridad', '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] | cebuaner |
6,334 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nanghinaot', 'si', 'Antigua', 'nga', 'ang', 'kahimanan', 'isip', 'parte', 'sa', 'programa', 'nga', 'no-contact', 'apprehension', 'dali', 'nga', 'makasikop', 'sa', 'nagpasagad', 'nga', 'motorista.', '(', 'JPP', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
6,335 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'usa', 'sa', 'tulo', 'ka', 'estruktura', 'nga', 'gipaguba', 'usa', 'ka', '14-door', 'apartment', 'nga', 'ginama', 'sa', 'light', 'materials', 'nga', 'gipanag-iya', 'ni', 'Cecilia', 'Freeze', 'nga', 'atua', 'sa', 'gawas', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,336 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pa-abangan', 'nga', 'gipanag-iya', 'ni', 'Paquibot', ',', 'usa', 'sa', 'mga', 'silingan', 'nagkanayon', 'nga', 'di', 'ang', 'opisyal', 'maoy', 'orihinal', 'nga', 'tag-iya', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,337 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'balay', 'gi-prenda', 'sa', 'tag-iya', 'diha', 'ni', 'Paquibot', 'gumikan', 'sa', 'emergency', 'nga', 'panginahanglan', 'hangtod', 'nga', 'wa', 'na', 'malukat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
6,338 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pakyas', 'ang', 'Superbalita', 'Cebu', 'sa', 'pagkuha', 'sa', 'habig', 'ni', 'Paquibot', 'alang', 'sa', 'kompirmasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 1, 0, 0, 0, 0] | cebuaner |
6,339 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpabilin', 'nga', 'malinawon', 'ang', 'demolition', 'kay', 'way', 'nikuwestiyon', 'o', 'suway', 'nga', 'misukol', 'sa', 'gihimo', 'sa', 'kagamhanan', 'sa', 'Lapu-Lapu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
6,340 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duha', 'ka', 'magtiayong', 'senior', 'citizen', 'ang', 'na-angol', 'gumikan', 'sa', 'pagkahulog', 'sa', 'dagat', 'sa', 'dihang', 'nalusno', 'ang', 'ilang', 'gipuy-an', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,341 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'gihimong', 'pakisusi', 'sa', 'City', 'Urban', 'Poor', 'nasuta', 'nga', 'moabot', 'sa', '72', 'ka', 'mga', 'balay', 'ang', 'illegal', 'nga', 'gitukod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,342 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'niadtongg', 'nagpaabang', 'adunay', 'mga', 'balay', 'nga', 'gipabarog', 'sa', 'baybayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,343 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mipasalig', 'ang', 'mayor', 'nga', 'mohatag', 'og', 'P10,000', 'nga', 'financial', 'aid', 'ug', 'relief', 'goods', 'ngadto', 'sa', 'house', 'owners', 'kansang', 'balay', 'gipaguba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,344 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iya', 'sab', 'nga', 'gisalikway', 'ang', 'na-unang', 'taho', 'sa', 'media', 'nga', 'tungod', 'sa', 'pagtadlas', 'sa', 'fastcraft', 'sa', 'Mactan', 'Channel', 'nga', 'maoy', 'naka-ingon', 'nga', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0] | cebuaner |
6,345 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'way', 'nakuhang', 'illegal', 'nga', 'drugas', 'ang', 'mga', 'operatiba', 'gawas', 'sa', 'mga', 'drug', 'paraphernalia', 'ug', '10', 'ka', 'cellular', 'phones', 'nga', 'gipanag', 'iya', 'sa', 'mga', '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, 0, 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 |
6,346 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Wa', 'na', 'masurpresa', 'si', 'Rubio', 'nga', 'wa', 'silay', 'makuha', 'nga', 'illegal', 'nga', 'drugas', 'sulod', 'sa', 'selda', 'sa', 'mga', 'binilango', 'nga', 'nalambigit', 'sa', 'illegal', 'nga', 'drugas', 'sanglit', 'matag', 'adlaw', 'ilang', 'gihimo', 'ang', 'surprise', '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, 0, 0, 0, 0, 0, 0, 0, 0, 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 |
6,347 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'nagpasabot', 'nga', 'way', 'drugas', 'nga', 'gipayuhot', 'agi', 'sa', 'jail', 'padulong', 'sa', 'gawas', 'kay', 'gikunsumo', 'na', 'lang', ',', 'samot', 'nga', 'kini', 'mahal', 'na', 'kaayo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,348 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Iyang', 'giangkon', 'nga', 'ang', 'mga', 'dalaw', 'ra', 'usab', 'ang', 'mosuway', 'og', 'payuhot', 'sa', 'sulod', 'sa', 'bilangoan', 'diin', 'ang', 'mga', 'suspek', 'mogamit', 'og', 'bata', 'aron', 'way', 'tulobagon', 'kon', 'masakpan', 'sa', 'jail', 'officers', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,349 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Niadtong', 'miaging', 'adlaw', 'usa', 'ka', 'bata', 'ang', 'ilang', 'nasakpan', 'nga', 'suway', 'unta', 'nga', 'mopasulod', 'sa', 'pinakete', '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] | cebuaner |
6,350 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'kahugot', 'sa', 'pagbantay', 'sa', 'entrance', ',', 'lisod', 'na', 'usab', 'ang', 'mga', 'kontrabando', 'sa', 'pagsulod', 'nga', 'usa', 'sa', 'hinungdan', 'nga', 'way', 'nakuha', '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, 0, 0] | cebuaner |
6,351 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kapin', 'sa', '50', 'ka', 'mga', 'pulis', 'ipakatap', 'sa', 'syudad', 'sa', 'Sugbo', 'gikan', 'sa', 'barangay', 'diin', 'gihaya', 'ang', 'kapitan', 'paingon', 'sa', 'lubnganan', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,352 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'hapon', 'gitakdang', 'ihatod', 'na', 'sa', 'lubnganan', 'si', 'Rupinta', 'human', 'kini', 'banhigi', 'sa', 'gituohan', 'nga', 'upat', 'ka', 'mga', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,353 | 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', 'kapulisan', 'mihan-ay', 'dayon', 'sa', 'ilang', 'paghatag', 'sa', 'siguridad', 'gikan', 'sa', 'barangay', 'gym', 'sa', 'Ermita', 'paingon', 'sa', 'simbahan', 'diin', 'siya', 'misahan', 'sa', 'katapusang', 'higayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,354 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Di', 'momenos', '50', 'ka', 'mga', 'pulis', 'gikan', 'sa', 'Carbon', 'police', 'station', 'ug', 'Mabolo', 'maoy', 'gitahasan', 'nga', 'mobantay', 'sa', 'rota', 'ug', 'area', 'security', 'kay', 'gituohan', 'daghang', 'supporters', 'niini', 'sa', 'Sugbo', 'ang', 'mohatod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
6,355 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bantayan', 'ang', 'mao', 'nga', 'karsada', 'sud', 'sa', '24', 'oras', 'sa', 'tibuok', 'semana', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,356 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kaniadtong', 'Sabado', 'gisugdan', 'na', 'ang', '“walk', 'through”', 'gikan', 'sa', 'Fuente', 'padulong', 'sa', 'Plaza', 'Independecia', 'og', 'giubanan', 'kini', 'sa', 'mga', '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, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,357 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Ronwaldo', 'Diloy', ',', 'head', 'sa', 'operations', 'sa', 'Pobe', ',', 'nga', 'adunay', 'vendors', 'nga', 'naapektohan', 'hilabi', 'na', 'sa', 'gitawag', 'og', '“lost', 'vendors”', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,358 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sab', 'sa', 'mga', 'plano', 'ang', 'pagpadaghan', 'sa', 'mga', 'personnel', 'nga', 'gikan', 'sa', 'duha', 'ka', 'shifts', ',', 'mahimong', 'tulo', ',', 'sugod', 'sa', 'buntag', ',', 'udto', 'ug', 'hangtod', 'sa', 'kadlawon', 'aron', 'mahimo', 'na', 'kining', '24', '/', '7', 'sa', 'pagbantay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
6,359 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Dr.', 'Rene', 'Catan', ',', 'pangu', 'sa', 'Provincial', 'Health', 'Office', ',', 'nipahibawo', 'niini', 'human', 'gikataho', 'nga', 'dunay', '10-anyos', 'nga', 'bata', 'sa', 'dakbayan', 'sa', 'Talisay', 'ang', 'giingong', 'nagka-dengue', 'ug', 'nalandig', 'sa', 'tambalanan', 'human', 'mabakunahi', 'og', 'Dengvaxia', 'vaccine', 'niadtong', 'Agusto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0] | cebuaner |
6,360 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'Department', 'of', 'Health', 'nihimakak', 'sa', 'mga', 'taho', 'nga', 'nagka-dengue', 'si', 'Jujen', 'Ababon', ',', 'Grade', '5', ',', 'taga', 'Sityo', 'Kaduldulan', ',', 'Barangay', 'Lawaan', '3', ',', 'Talisay', 'human', 'mahatagi', 'og', 'Dengvaxia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 7, 0] | cebuaner |
6,361 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Bernadas', 'nipasabot', 'nga', 'si', 'Ababon', 'na-dengue', 'kay', 'napaakan', 'og', 'Aedes', 'Aegypti', 'nga', 'lamok', 'ug', 'dili', 'tungod', 'sa', 'Dengvaxia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 7, 0] | cebuaner |
6,362 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niini', 'nga', 'sanglit', 'wala', 'makumpleto', 'sa', 'bata', 'ang', 'tulo', 'ka', 'doses', 'sa', 'Dengvaxia', ',', 'dili', 'lig-on', 'ang', 'resistensya', 'niini', 'sa', 'upat', 'ka', 'strains', 'sa', 'dengue', 'fever', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,363 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Catan', 'niangkon', 'nga', 'wala', 'pa', 'silay', 'integrated', 'reporting', 'system', 'sa', 'kasamtangan', 'sa', 'mga', 'bata', 'nga', 'nabakunahan', 'og', 'Dengvaxia', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 7, 0] | cebuaner |
6,364 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'nini', ',', 'maghimo', 'sila', 'og', 'data', 'base', 'sa', '159,766', 'ka', 'mga', 'bata', 'nga', 'nabakunahan', 'sa', 'unang', 'dose', 'sa', 'Dengvaxia', 'dinhi', '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, 7, 0, 0, 5, 0] | cebuaner |
6,365 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tumong', 'niini', 'nga', 'mamonitor', 'nila', 'ug', 'mapaubos', 'sa', 'surveillance', 'ang', 'mga', 'bata', 'ngsa', 'nabakunahan', 'na', 'ug', 'ila', 'kining', 'pangitaon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,366 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ababon', 'usa', 'sa', 'mga', 'nakapahimulos', 'sa', 'bakuna', 'sa', 'Dengvaxia', 'niadtong', 'Agusto', '8', 'sa', 'dihang', 'gihimo', 'ang', 'pagpangbakuna', 'mismo', '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, 1, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,367 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagkanayon', 'si', 'Aznar', 'nga', 'nihimo', 'na', 'karon', 'og', 'pakisusi', 'ang', 'ilang', 'city', 'health', 'office', 'pinangulohan', 'ni', 'Dr.', 'Lino', 'Alanzado', 'sa', 'Lawaaan-3', 'samtang', 'ila', 'nang', 'giawhag', 'ang', 'ginikanan', 'nga', 'bisan', 'og', 'makagawas', 'na', 'ang', 'bata', ',', 'padayon', 'gihapon', 'nga', 'i-monitor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 4, 4, 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, 0] | cebuaner |
6,368 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipahibawo', 'usab', 'ni', 'Felipa', 'Solana', ',', 'pangulo', 'sa', 'city', 'social', 'welfare', 'and', 'services', ',', 'nga', 'ang', 'kagamhanan', 'sa', 'Talisay', 'andam', 'mohatag', 'og', 'hinabang', 'pinansyal', 'ngadto', 'sa', 'mga', 'ginikanan', 'sa', 'bata', 'hangtod', 'sa', 'P10,000', '.'] 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, 0, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,369 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikatahong', 'luwas', 'na', 'sa', 'makuyaw', 'nga', 'kahimtang', 'ang', 'bata', 'ug', 'padayon', 'nagpaalim', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,370 | 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', 'Delia', 'Ybañez', 'sa', 'Lawaan-3', 'niingon', 'nga', 'sa', 'sinugdanan', 'pa', 'lang', 'nagsegig', 'na', 'siyag', 'pangutana', 'labot', 'sa', 'epekto', 'sa', 'maong', 'bakuna', 'sa', 'dihang', 'gihimo', 'kini', 'sa', 'iyang', 'barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 1, 2, 0, 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] | cebuaner |
6,371 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'karon', 'nga', 'migawas', 'ang', 'report', 'sa', 'di', 'maayo', 'nga', 'resulta', 'ug', 'diha', 'pa', 'sa', 'iyang', 'barangay', 'ang', 'unang', 'bata', 'sa', 'Talisay', 'nga', 'nabakunahan', 'ug', 'nataptan', 'sa', 'dengue', ',', 'kalagot', 'ug', 'kahadlok', 'ang', 'iyang', 'gibati', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,372 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'usab', 'ni', 'Konsehal', 'Aznar', 'ang', 'taga', 'Talisay', 'nga', 'kon', 'gihilantan', 'ang', 'ilang', 'anak', 'dad-on', 'gilayon', 'kini', 'sa', 'ilang', 'Rural', 'Health', 'Units', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0] | cebuaner |
6,373 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'unang', 'mga', 'bata', 'nga', 'nabakunahan', 'og', 'dengue', 'vaccine', 'Dengvaxia', 'nidangat', 'sa', 'tambalanan', 'human', 'giingong', 'nasakit', 'og', 'dengue', 'fever', 'sukad', 'sa', 'miaging', 'semana', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,374 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidala', 'og', 'una', 'sa', 'Talisay', 'District', 'Hospital', 'una', 'pa', 'gibalhin', 'sa', 'laing', 'tambalanan', 'si', 'Jujen', 'Ababon', '10', ',', 'tinun-an', 'sa', 'grade-5', ',', 'taga', 'sityo', 'Kaduldulan', ',', 'Barangay', 'Lawaan', '3', ',', 'dakbayan', 'sa', 'Talisay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
6,375 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nabantayan', 'sa', 'iyang', 'inahan', 'nga', 'si', 'Jinny', 'Ababon', ',', '30', ',', 'nga', 'naghilanat', 'sukad', 'pa', 'niadtong', 'Sabado', 'ang', 'iyang', 'anak', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,376 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'sa', 'pagka', 'Domingo', ',', 'nagsigi', 'na', 'kini', 'og', 'sunggo', 'apan', 'iyang', 'gitambalan', '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 |
6,377 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Jinny', 'nga', 'niadtong', 'Martes', 'nahuwasan', 'ang', 'ilang', 'kahingawa', 'tungod', 'kay', 'wa', 'na', 'kini', 'hilanti', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,378 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'kagahapon', 'sa', 'kadlawon', 'ilang', 'nabantayan', 'nga', 'migrabe', 'ang', 'sunggo', 'sa', 'iyang', 'anak', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,379 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakahukom', 'sila', 'nga', 'ila', 'kining', 'dad-on', 'sa', 'district', 'hospital', 'ug', 'didto', 'nila', 'nasayran', 'nga', 'nasakit', 'kinig', 'dengue', 'fever', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 7, 8, 0] | cebuaner |
6,380 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibutyag', 'ni', 'Jinny', 'nga', 'ang', 'iyang', 'anak', 'usa', 'sa', 'mga', 'nakapahimulos', 'sa', 'bakuna', 'nga', 'Dengvaxia', 'niadtong', 'Agusto', '8', 'ning', 'tuiga', 'sa', 'dihang', 'gihimo', 'ang', 'pagpangbakuna', 'sa', 'Talisay', 'City', 'Health', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0] | cebuaner |
6,381 | 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', 'human', 'sila', 'makadungog', 'sa', 'balita', 'sa', 'epekto', 'sa', 'bakuna', ',', 'siya', 'nabalaka', 'ilabi', 'na', 'kay', 'ang', 'iyang', 'anak', 'wa', 'pa', 'usab', 'makasuway', 'og', 'dengue', 'hinungdan', 'nga', 'misamot', 'ang', 'iyang', 'kabalaka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,382 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['“Akong', 'giingnan', 'akong', 'anak', 'nga', 'ayaw', 'sigi', 'laag', 'kay', 'mag', 'monitor', 'ko', 'nimo.', 'Naa', 'baya', 'mga', 'balita.', 'Nga', 'bisag', 'mahilantan', 'kiat', 'man', 'siya', 'gihapon', ',', '”matod', 'sa', 'inahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,383 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Sanofi', 'Pasteur', 'nipahibawo', 'sa', 'resulta', 'sa', 'ilang', 'mga', 'pagtuon', 'nga', 'ang', 'bata', 'nga', 'wa', 'pa', 'makasuway', 'og', 'dengue', 'fever', 'apan', 'nabakunahan', ',', 'masakit', 'og', '“severe', 'dengue.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,384 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mas', 'maayo', 'nga', 'madapatan', 'niini', 'nga', 'nakaagi', 'sa', 'mao', 'nga', 'sakit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,385 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Department', 'of', 'Health', 'nipahunong', 'una', 'sa', 'pagpamakuna', 'human', 'sa', 'pahibawo', 'sa', 'manufacturer', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,386 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'head', 'nurse', 'Talisay', 'District', 'Hospital', 'nga', 'si', 'Harby', 'Abellanosa', 'miingon', 'nga', 'anaa', 'na', 'sa', 'stable', 'nga', 'kahimtang', 'ang', '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, 3, 4, 4, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,387 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'gibalhin', 'lang', 'usab', 'kini', 'dayon', 'sa', 'pribadong', 'tambalanan', 'alang', 'sa', 'dugang', 'pag-atiman', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,388 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Konsehal', 'Choy', 'Aznar', ',', 'pangu', 'sa', 'committee', 'on', 'health', 'sa', 'city', 'council', 'sa', 'Talisay', ',', 'niingon', 'nga', 'ila', 'nang', 'gipa-monitor', 'sa', 'doktor', 'ang', 'kahimtang', 'sa', '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, 1, 2, 0, 0, 0, 3, 4, 4, 0, 3, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,389 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mohatag', 'usab', 'sila', 'og', 'financial', 'assistance', 'ngadto', 'sa', 'pamilya', 'kon', 'gikinahanglan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,390 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nabalaka', 'si', 'Cebu', 'Archbishop', 'Jose', 'Palma', 'sa', 'unsay', 'mahitabo', 'sa', 'kabataan', 'nga', 'nabakunahan', 'og', 'Dengvaxia', 'vaccine', 'human', 'nipahibawo', 'ang', 'Sanofi', 'Pasteur', 'nga', 'makasamot', 'kini', 'sa', 'sakit', 'kon', 'wala', 'pa', 'magka-dengue', 'ang', '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, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,391 | 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', 'gibakunahan', 'ang', 'kabataan', ',', 'matod', 'niini', ',', 'angay', 'nga', 'kadaghan', 'nga', 'gisuwayan', 'ang', 'Dengvaxia', 'aron', 'maseguro', 'ang', 'kaluwas', '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, 7, 0, 0, 0, 0, 0, 0] | cebuaner |
6,392 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nidugang', 'nga', 'bisan', 'kon', 'maayo', 'ang', 'intensyon', 'apan', 'kon', 'wa', 'masuwayi', 'og', 'kadaghan', ',', 'delikado', 'nga', 'makahatag', 'og', 'komplikasyon', 'sa', 'mga', 'nabakunahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
6,393 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Food', 'and', 'Drug', 'Administration', 'nimando', 'na', 'sa', 'Sanofi', 'Pasteur', 'sa', 'paghunong', 'og', 'baligya', 'ug', 'pag-apudapod', 'sa', 'Dengvaxia', 'vaccine', 'sa', 'Pilipinas', 'human', 'kini', 'nipagawas', 'og', 'advisory', 'bag-ohay', 'lang', 'nga', 'mosamot', 'ang', 'sakit', 'sa', 'bata', 'kon', 'nabakunahan', 'niini', 'nga', 'wa', 'makasuway', 'og', 'ka-dengue', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,394 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'ang', 'maong', 'kantidad', 'kuhaan', 'sa', 'City', 'Government', 'og', 'P5,000', 'atol', 'sa', 'distribution', 'karong', 'Disyembre', '16', 'aron', 'mahinayhinayan', 'og', 'bayad', 'sa', 'mga', 'magtutudlo', 'ang', 'ilang', 'nakobra', 'sa', 'cost', 'of', 'living', 'allowance', '(', 'Cola', ')', 'niadtong', '2015', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0] | cebuaner |
6,395 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'mga', 'magtutudlo', 'nga', 'mo-retire', 'karon', 'ug', 'sunod', 'tuig', 'di', 'makakobra', 'sa', 'P5,000', 'sanglit', 'ang', 'P10,000', 'nga', 'ihatag', 'sa', 'Siyudad', 'kuwang', 'pa', 'nga', 'ibayad', 'sa', 'Cola', 'depende', 'sa', 'gidak-on', '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, 7, 0, 0, 0, 0, 0] | cebuaner |
6,396 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'ilang', 'madawat', 'nga', 'P10,000', 'itingob', 'og', 'kunhod', 'sa', 'ilang', 'Cola', 'unta', ',', 'apan', 'wa', 'nisugot', 'ang', 'konsehal', ',', 'inay', 'pahinayhinayan', 'kini', 'og', 'bayad', 'sa', 'mga', 'maestra', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
6,397 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'nahunahunaan', 'ang', 'tag', 'P2,000', 'matag', 'tuig', ',', 'apan', 'wa', 'mosugot', 'ang', 'City', 'Accounting', 'Office', 'kay', 'molanat', 'pa', 'og', 'siyam', 'ka', 'tuig', 'sa', 'di', 'pa', 'maimpas', 'hinungdan', 'nga', 'nasabutan', 'nila', 'nga', 'tag', 'P5,000', 'matag', 'tuig', 'ang', 'i-data', 'hangtod', 'maimpas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 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 |
6,398 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hinuon', 'gipadayag', 'ni', 'Young', 'nga', 'magdepende', 'kini', 'sa', 'mga', 'maestra', 'sanglit', 'di', 'tanan', 'kanila', 'nakadawat', 'sa', 'tag', 'P18,000', 'kay', 'ang', 'uban', 'ubos', 'ra', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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] | cebuaner |
6,399 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'paghatag', 'sa', 'P10,000', 'nga', 'financial', 'assistance', 'himuon', 'sa', 'City', 'Hal', 'matag', 'tuig', 'sanglit', 'gipaluyohan', 'kini', 'og', 'ordinansa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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