Unnamed: 0 int64 0 335k | question stringlengths 17 26.8k | answer stringlengths 1 7.13k | user_parent stringclasses 29 values |
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5,300 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'tungod', 'sa', 'Bagyong', 'Vinta', ',', 'nalangay', 'ang', 'iyang', 'pag-uli', ',', 'apil', 'na', 'ang', 'liboan', 'ka', 'mga', 'pasahero', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,301 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tungod', 'sa', 'kadagko', 'sa', 'mga', 'bawod', ',', 'gihakop', 'sila', 'sa', 'kahadlok', ',', 'apan', 'nagpalasamat', 'sila', 'nga', 'nibalik', 'ang', 'Oceanjet', 'sa', 'agi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 3, 0, 0, 0] | cebuaner |
5,302 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', '30', 'ka', 'flights', 'ang', 'gipangkansilar', 'usab', 'ang', 'mga', 'biyahe', 'gikan', 'sa', 'Mactan', 'Cebu', 'International', 'Airport', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0] | cebuaner |
5,303 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'subay', 'usab', 'sa', 'gikanselar', 'nga', 'biyahe', 'sa', 'mga', 'sakyanan', 'sa', 'kadagatan', 'paingon', 'sa', 'maong', 'mga', 'dapit', ',', 'sa', 'habagatang', 'bahin', '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, 0, 5, 0] | cebuaner |
5,304 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigun', 'sa', 'CSBT', ',', 'mas', 'maayong', 'magpabilin', 'sila', 'sa', 'terminal', 'inay', 'nga', 'magtapok', 'sa', 'pantalan', 'ug', 'didto', 'mangatanggong', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,305 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugod', 'niadtong', 'Huwebes', ',', 'wa', 'nay', 'bus', 'paingon', 'sa', 'Dumaguete', ',', 'Bacolod', 'ug', 'Zamboanga', 'City', 'nga', 'gitugotan', 'nga', 'makabiya', '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, 5, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 3, 0] | cebuaner |
5,306 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'maong', 'mga', 'bus', 'ikarga', 'og', 'barge', 'paingon', 'sa', 'ilang', 'mga', 'destinasyon', 'apan', 'tungod', 'kay', 'kanselado', 'ang', 'biyahe', 'sa', 'mga', 'sakyanan', 'sa', 'kadagatan', ',', 'dili', 'ra', 'gihapon', 'sila', 'makatabok', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,307 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'bagyong', 'Vinta', 'kay', 'nagkahinay', 'nahimo', 'nang', 'tropical', 'depression', 'nga', 'nagdala', 'sa', 'hangin', 'nga', 'may', 'gikusgon', 'nga', '60', 'ka', 'kilometros', 'matag', 'takna', 'duol', 'sa', 'iyang', 'sentro', 'ug', 'pag-unos', 'nga', 'moabot', 'ngadto', 'sa', '90', 'ka', 'kilometros', 'matag', 'oras', 'samtang', 'nag-irog', 'sa', 'direksyon', 'nga', 'kasadpan', 'sa', 'gikusgon', 'nga', '20', 'ka', 'kilometros', 'matag', '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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,308 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'nakatugpa', 'sa', 'yuta', 'sa', 'Cateel', ',', 'Davao', 'Oriental', 'ala', '1:45', 'kagahapon', 'sa', 'kaadlawon', 'ug', 'naa', 'sa', 'Zamboanga', 'Del', 'Sur', 'sa', 'pagkutlo', 'ning', 'balita', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
5,309 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'gialerto', 'nila', 'ang', 'mga', 'local', 'government', 'unit', 'ug', 'mga', 'molupyo', 'labi', 'na', 'sa', 'mga', 'dapit', 'nga', 'delikado', 'sa', 'pagdahili', 'sa', 'yuta', 'ug', 'flashfloods', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,310 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Tribunalo', 'nidasig', 'sa', 'mga', 'LGU', 'ug', 'local', 'DRRMO', 'nga', 'gamiton', 'ang', 'mga', 'mapa', 'nga', 'giapud-apod', 'nga', 'nag-ila', 'sa', 'mga', 'dapit', 'nga', 'peligro', 'sa', 'kalamidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,311 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'Liloan', 'Port', 'sa', 'lungsod', 'sa', 'Santander', ',', 'dunay', 'gibanabanang', '500', 'ka', 'mga', 'pasahero', 'ang', 'nangatanggong', 'ug', 'gihatagan', 'og', 'food', 'packs', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,312 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'lungsod', 'sa', 'Oslob', ',', 'dunay', '46', 'ka', 'lokal', 'nga', 'mga', 'turista', ',', 'unom', 'niini', 'mga', 'bata', 'ug', 'walo', 'ka', 'langyaw', 'nga', 'mga', 'turista', ',', 'usa', 'bata', 'ang', 'wa', 'makatabok', 'gikan', 'sa', 'isla', 'sa', 'Sumilon', 'paingon', 'sa', 'mainland', 'gumikan', '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, 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, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,313 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Masinati', 'sa', 'mga', 'konsumedor', 'sa', 'Metropolitan', 'Cebu', 'Water', 'District', '(', 'MCWD', ')', 'ang', '10', 'ka', 'oras', 'nga', 'way', 'supply', 'sa', 'tubig', 'sa', 'pipila', 'ka', 'mga', 'barangay', 'sa', 'mga', 'dakbayan', 'sa', 'Sugbo', 'ug', 'Talisay', 'sugod', 'sa', 'gabii', 'sa', 'Disyembre', '28', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,314 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Kara', 'nipasabot', 'nga', 'kinahanglan', 'isibog', 'nila', 'ang', 'dagko', 'nga', 'mga', 'tubo', ',', 'putlon', 'ug', 'isumpay', 'og', 'balik', 'aron', 'dili', 'mahugawan', 'ang', 'supply', 'sa', 'tubig', 'nga', 'mosubay', 'sa', 'maong', 'mga', 'tubo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,315 | 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', 'dili', 'sila', 'dihadiha', 'makabalhin', 'sa', 'mga', 'tubo', 'kay', 'mag-agad', 'sila', 'sa', 'contractor', 'sa', 'DPWH', 'kon', 'mahuman', 'na', 'og', 'kubkob', 'sa', 'yuta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 0, 0, 0] | cebuaner |
5,316 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Kara', 'nangayo', 'sa', 'pagsabot', 'sa', 'mga', 'konsumedor', 'bahin', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,317 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bisan', 'sa', 'mga', 'pagsulay', 'nga', 'nasinati', 'sa', 'nasod', 'sama', 'sa', 'kalamidad', 'ug', 'kalisod', ',', 'anaa', 'kanunay', 'ang', 'Ginuo', ',', 'pagpahinumdom', 'ni', 'Cebu', 'Archbishop', 'Jose', 'Palma', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 5, 0, 1, 2, 0] | cebuaner |
5,318 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'iyang', 'mensahe', 'alang', 'sa', 'Pasko', ',', 'ang', 'arsobispo', 'namulong', 'nga', 'nasuwayan', 'ang', 'kahiusahan', 'sa', 'nasod', 'sa', 'mga', 'panghitabo', 'sama', 'sa', 'pag-atake', 'sa', 'mga', 'ISIS-Maute', 'Group', 'sa', 'siyudad', 'sa', 'Marawi', 'nga', 'nikutlo', 'og', 'daghang', 'kinabuhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0] | cebuaner |
5,319 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'arsobispo', 'namahayag', 'ang', 'Pasko', 'nagpahinumdom', 'sa', 'dihang', 'gihimugso', 'si', 'Hesukristo', 'diin', 'way', 'pamilya', 'ang', 'nidawat', 'nila', 'ni', 'Jose', 'ug', 'Maria', 'aron', 'didto', 'manganak', 'ang', 'inahan', 'sa', 'Ginuo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,320 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Siya', 'nidasig', 'sa', 'mga', 'tawo', 'nga', 'dawaton', 'si', 'Hesus', 'diha', 'sa', 'ilang', 'kasingkasing', ',', 'sa', 'ilang', 'pamilya', 'ug', 'komunidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,321 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Suma', 'pa', 'niini', 'nga', 'nasayod', 'ang', 'mga', 'tawo', 'nga', 'kon', 'ang', 'Ginuo', 'anaa', 'nila', ',', 'dunay', 'kausaban', 'alang', 'sa', 'kaayo', 'nga', 'moepekto', 'sa', 'gugma', ',', 'kaluoy', 'ug', 'kapasayloan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
5,322 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Palma', 'nidugang', 'nga', 'sama', 'ni', 'Hesus', 'nga', 'gasa', 'gikan', 'sa', 'Ginuo', ',', 'unta', 'ang', 'mga', 'tawo', 'mahimo', 'usab', 'nga', 'instrumento', 'sa', 'paglaom', 'ug', 'kamaya', 'ug', 'gasa', 'ngadto', 'sa', 'uban', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,323 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ahmed', 'Cuizon', ',', 'director', 'sa', 'LTFRB', '7', ',', 'nibutyag', 'nga', 'moabot', 'sa', '66', 'ka', 'special', 'permits', 'ang', 'giisyu', 'sa', 'mga', 'bus', 'samtang', '24', 'sa', 'mga', 'minibus', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 2, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,324 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'special', 'permits', 'gikinahanglan', 'aron', 'makalabyog', 'ang', 'publiko', 'nga', 'mga', 'sakyanan', 'gawas', 'sa', 'gitugot', 'nga', 'rota', 'alang', 'niini', ',', 'aron', 'makahakot', 'og', 'mga', 'pasahero', 'ug', 'dili', 'dakpon', 'sa', 'Land', 'Transportation', 'Office', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 3, 4, 4, 0] | cebuaner |
5,325 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'niya', 'lima', 'ka', 'operators', 'ang', 'nag-apply', 'aron', 'maisyuhan', 'sa', 'special', 'permits', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,326 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kini', 'naglakip', 'sa', 'Vallacar', 'Transit', ',', 'Inc', ',', 'ang', 'kompaniya', 'nga', 'nagpadagan', 'sa', 'Ceres', 'buses', ';', 'Sugbo', 'Transit', 'Express', ';', 'Island', 'Autobus', 'Transit', 'Corp.', ';', 'ug', 'Metro', 'Cebu', 'Autobus', 'Corp', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 3, 4, 0, 3, 4, 4, 0, 3, 4, 4, 4, 0, 0, 3, 4, 4, 4, 4] | cebuaner |
5,327 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kasagaran', 'sa', 'mga', 'special', 'permit', 'balido', 'sud', 'sa', '12', 'ka', 'adlaw', 'kun', 'sugod', 'karong', 'adlawa', 'hangtod', 'sa', 'Enero', '3', 'duna', 'usab', 'unom', 'ka', 'adlaw', 'kun', 'kutob', 'sa', 'Disyembre', '28', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,328 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kinatibuk-an', ',', '55', 'ka', 'special', 'permits', 'ang', 'giisyu', 'sa', 'LTFRB', '7', 'alang', 'sa', 'mga', 'bus', 'ug', '24', 'ka', 'special', 'permits', 'sa', 'mga', 'minibus', 'sa', 'Vallacar', ',', '6', 'sa', 'Sugbo', 'Transit', 'Express', ',', '1', 'sa', 'Mabel', 'Montenegro', ',', '1', 'sa', 'Island', 'Autobus', 'ug', '4', 'sa', 'Metro', 'Cebu', 'Autobus', 'Corp.', '(', 'SCG', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 4, 4, 0, 0, 0, 3, 4, 0, 0, 0, 3, 4, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4] | cebuaner |
5,329 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Osmeña', 'namahayag', 'nga', 'bisan', 'ang', 'mga', 'kabos', 'kinahanglan', 'nga', 'mobayad', 'og', 'buhis', 'sa', 'ilang', 'kinitian', 'bisan', 'kon', 'gamay', 'ra', 'nga', 'kantidad', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,330 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Osmeña', 'kinsa', 'nimando', 'sa', 'tag', 'P5', 'nga', 'bayranan', 'sa', 'mga', 'drayber', 'sa', 'motorsiklo', 'nga', 'makalapas', 'sa', 'lagda', 'sa', 'trapiko', 'pinaagi', 'sa', 'adjudication', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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 |
5,331 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihulagway', 'niya', 'nga', 'di', 'maayo', 'ang', 'pipila', 'sa', 'mga', 'provision', 'nga', 'nasukip', 'sa', 'Train', 'nga', 'mo-epekto', 'sugod', 'tuig', '2018', 'nga', 'iyang', 'gihulagway', 'nga', 'makadaot', 'sa', 'nasod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,332 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Subay', 'sa', 'Train', ',', 'gawas', 'nga', 'way', 'buhis', 'ang', 'nagsweldo', 'og', 'P250,000', 'matag', 'tuig', ',', 'mosaka', 'ang', 'presyo', 'sa', 'gasolina', 'ug', 'krudo', ',', 'dugang', 'buhis', 'sa', 'mga', 'sakyanan', ',', 'coal', 'ug', 'sugar', 'sweetened', 'drinks', 'sama', 'sa', 'mga', 'soft', 'drinks', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,333 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'buhis', 'nga', 'makolekta', 'sa', 'Train', 'maoy', 'igasto', 'sa', 'P8', 'trilyones', 'nga', 'proyekto', 'sa', 'imprastraktura', 'sa', 'kasamtangang', 'administrasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,334 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sugod', 'tuig', '2018', ',', 'ang', 'produkto', 'nga', 'diesel', 'buhisan', 'og', 'P2.50', 'matag', 'litro', 'hangtod', 'mosaka', 'sa', 'P4.50', 'sa', 'tuig', '2019', 'ug', 'mahimong', 'P6', 'sa', 'tuig', '2020', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,335 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Liquified', 'Petroleum', 'Gas', '(', 'LPG', ')', 'buhisan', 'og', 'P1', 'matag', 'kilo', 'sa', 'tuig', '2018', ',', 'P2', 'sa', '2019', 'ug', 'P3', 'sa', '2020', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,336 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'gobyerno', 'mokolekta', 'og', 'P7', 'nga', 'buhis', 'sa', 'matag', 'litro', 'sa', 'gasolina', ',', 'P9', 'sa', 'tuig', '2019', 'ug', 'P10', 'sa', '2020.', '(', 'PAC', ')'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,337 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Hatagan', 'og', 'prayoridad', 'ni', 'Mayor', 'Tomas', 'Osmeña', 'sa', 'tuig', '2018', 'ang', 'pagtukod', 'og', 'dormitory', 'para', 'sa', '100', 'ka', 'mga', 'single', 'mother', 'kinsa', 'nagtrabaho', 'sa', 'business', 'process', 'outsourcing', '(', 'BPO', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,338 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Osmeña', 'nga', 'daghan', 'siya', 'og', 'ipatuman', 'nga', 'proyekto', 'sunod', 'tuig', 'apan', 'kini', 'maoy', 'iyang', 'gihatagan', 'og', 'gibug-aton', 'sanglit', 'iyang', 'naobserbahan', 'nga', 'way', 'maayong', 'kaugmaon', 'ang', 'mga', 'single', 'mother', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,339 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipasabot', 'sa', 'mayor', 'nga', 'dakong', 'hagit', 'alang', 'kaniya', 'sanglit', 'ang', 'mga', 'inahan', 'nga', 'maoy', 'nagmatuto', 'sa', 'ilang', 'mga', 'anak', 'nga', 'way', 'amahan', 'di', 'maglisod', 'og', 'trabaho', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,340 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'dormitory', 'nga', 'tukoron', 'ni', 'Osmeña', 'makaatiman', 'og', '100', 'ka', 'single', 'mother', 'diin', 'tuyo', 'sa', 'dormitory', 'mao', 'nga', 'usahon', 'ang', 'mga', 'bata', ',', 'lainon', 'ang', 'lalaki', 'ug', 'babaye', 'apan', 'usa', 'ra', 'ang', 'ilang', 'kusina', ',', 'adunay', 'playground', 'ug', 'ubang', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,341 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kon', 'makita', 'niya', 'nga', 'maayo', 'ang', 'lakat', 'sa', 'programa', ',', 'modugang', 'og', 'laing', 'mga', 'dormitoryo', 'si', 'Osmeña', 'aron', 'maserbisyohan', 'niya', 'ang', 'daghang', 'mga', 'single', 'mother', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,342 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Tuyo', 'ni', 'Osmeña', 'nga', 'samtang', 'nagtrabaho', 'ang', 'ilang', 'mga', 'inahan', ',', 'adunay', 'mag-alima', 'sa', 'ilang', 'mga', 'anak', 'ug', 'di', 'na', 'sila', 'angay', 'mabalaka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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] | cebuaner |
5,343 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'biktima', 'giila', 'nga', 'si', 'Mark', 'Neil', 'Mondejar', ',', '25', 'anyos', 'ug', 'residente', 'sa', 'Cogon', ',', 'A', 'Lopez', 'St.', ',', '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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 5, 0] | cebuaner |
5,344 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Mondejar', 'nagbaklay', 'daplin', 'sa', 'dan', 'sa', 'naasoyng', 'lugar', 'kuyog', 'sa', 'iyang', 'higala', 'nga', 'si', 'Jay', 'Boy', 'Quijano', 'human', 'kini', 'sila', 'makigtagay', 'sa', 'parehong', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,345 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'dihang', 'ang', 'duha', 'ka', 'wala', 'mailhing', 'mga', 'lalaki', 'nga', 'nagsakay', 'og', 'motor', 'nga', 'giilang', 'mga', 'suspek', 'mitungha', 'sa', 'atubangan', 'nila', 'ni', 'Mondejar', 'ug', 'Quijano', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0] | cebuaner |
5,346 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'biktima', 'naigo', 'sa', 'tuo', 'nga', 'bahin', 'sa', 'iyang', 'bukton', 'ug', 'kini', 'nakaaghat', 'kaniya', 'sa', 'pag-ikyas', 'samtang', 'ang', 'mga', 'suspek', 'nagdali-dali', 'og', 'pag-ikyas', 'paingon', 'sa', 'laing', 'direksyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,347 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giparesentar', 'sa', 'PRO', '7', 'ang', 'ilang', 'pagpangandam', 'sa', 'siguridad', 'sa', 'simbahan', 'sama', 'sa', 'pag', 'sira', 'sa', 'karsada', 'sa', 'Osmena', 'boulevard', 'gikan', 'sa', 'tanang', '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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0] | cebuaner |
5,348 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'ilang', 'plano', 'nga', 'didto', 'ibutang', 'sa', 'dan', 'Lapu-lapu', 'ang', 'mga', 'manindahay', 'ug', 'imahen', 'aron', 'di', 'sila', 'makabalda', 'sa', 'mga', 'tawo', 'nga', 'mosud', 'ug', 'mogawas', 'sa', 'simbahan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,349 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Aron', 'mahimong', 'hapsay', 'ang', 'pagsud', 'ug', 'paggawas', 'sa', 'mga', 'debotong', 'Katoliko', 'ang', 'tanang', 'entrada', 'sa', 'simbahan', 'didto', 'na', 'sa', 'luyong', 'bahin', 'ug', 'kilid', 'sa', 'Basilica', 'samtang', 'ang', 'gawsanan', 'mao', 'ang', 'mga', 'pultahan', 'nga', 'nag', 'atubang', 'sa', 'Osmena', 'Boulevard', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,350 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipusil', 'patay', 'ang', 'barangay', 'tanod', 'sa', 'iyang', 'gikaduwag', 'hantak', 'human', 'gisungog', 'dihang', 'napildi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,351 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nahitabo', 'ang', 'insidente', 'niadtong', 'Huwebes', 'sa', 'gabii', 'sa', 'Barangay', 'Tapul', ',', 'dakbayan', 'sa', 'Talisay', ',', 'Sugbo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 6, 6, 0] | cebuaner |
5,352 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'ang', 'suspek', 'mao', 'si', 'Robert', 'Castanares', ',', '26', ',', 'ug', 'pulos', 'taga', 'didto', 'ra', 'usab', 'sa', 'maong', 'lugar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,353 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'paghiabot', 'sa', 'kapulisan', 'nagbuy-od', 'na', 'ang', 'biktima', 'ug', 'aduna', 'nay', 'samad', 'sa', 'ulo', 'ug', 'bukton', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,354 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'pa', 'nagsugal', 'ang', 'duha', 'og', 'hantak', 'sa', 'dihang', 'napildi', 'ang', 'suspek', 'gisungog', 'kini', 'sa', 'biktima', 'nga', 'miresulta', 'sa', 'pagpamusil', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,355 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'suspek', 'daling', 'misibat', 'samtang', 'nakuha', 'gikan', 'sa', 'crime', 'scene', 'ang', 'tulo', 'ka', 'kabhang', 'sa', 'giutohang', 'kalibre', '.45', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,356 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'sa', 'buntag', 'ang', 'suspek', 'bulontaryong', 'mitahan', 'sa', 'iyang', 'kaugalingon', 'ngadto', 'sa', 'kapulisan', 'sa', 'Talisay', 'inubanan', 'sa', 'laing', 'pulis', 'nga', 'taga', 'didto', 'ra', 'usab', 'nga', 'si', 'P01', 'Robert', 'Sabequil', 'ug', 'ni', 'Konsehal', 'Marvin', 'Lapinid', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 2, 0, 0, 0, 1, 2, 0] | cebuaner |
5,357 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Kagahapon', 'gihimo', 'ang', 'paraffin', 'test', 'sa', 'suspek', 'samtang', 'gipasakaan', 'na', 'kini', 'kaso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,358 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Philippine', 'Drug', 'Enforcement', 'Agency', '(', 'PDEA', ')', '8', 'uban', 'sa', 'mga', 'gwardiya', 'sa', 'bilanggoan', 'ang', 'mihimo', 'og', 'inspeksyon', ',', 'alas', '9', 'sa', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,359 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giila', 'ang', 'lima', 'nga', 'sila', 'Mario', 'Marmita', ',', 'sakop', 'sa', 'Omega', 'Drug', 'Group', ';', 'Noli', 'Rinos', ';', 'Allan', 'Sia', ';', 'Elvira', 'Cuevas', 'ug', 'Jonalyn', 'Culaba', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 4, 4, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0] | cebuaner |
5,360 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nasakmit', 'sa', 'PDEA', 'ang', 'usa', 'ka', 'inablihan', 'nga', 'pakite', 'nga', 'dunay', 'shabu', ',', 'duha', 'ka', 'foil', 'nga', 'dunay', 'shabu', 'ug', 'uban', 'gamit', 'sa', 'pagsuyop', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,361 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nadugangan', 'ang', 'kaso', 'sa', 'lima', 'ka', 'piniriso', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,362 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giunay', 'pagdunggab', 'ang', 'usa', 'ka', '76', 'anyos', 'nga', 'lolo', 'sa', 'iyang', 'kauban', 'sa', 'tagay', 'dihang', 'miinit', 'ang', 'panaglalis', 'sa', 'duha', 'samtang', 'nag-inom', 'didto', 'sa', 'Brgy.', 'San', 'Vicente', ',', 'lungsod', 'sa', 'Dagohoy', '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, 0, 5, 6, 6, 0, 0, 0, 5, 0, 0, 0] | cebuaner |
5,363 | 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', 'Felix', 'Soliano', ',', 'mag-uuma', 'ug', 'residente', 'sa', 'Purok', '4', ',', 'Barangay', 'San', 'Vicente', ',', 'lungsod', 'sa', 'Dagohoy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 5, 0] | cebuaner |
5,364 | 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', 'sa', ',', 'nasayran', 'nga', 'ang', 'duha', 'nagtagay', 'kauban', 'sa', 'ilang', 'laing', 'mga', 'higala', 'sa', 'usa', 'ka', 'tindahan', 'sa', 'nahisgutang', 'barangay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,365 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'sa', 'lima', 'nga', 'nasakpan', 'ang', 'nakuhaan', 'og', 'laing', 'usa', 'ka', 'putos', 'nga', 'gituhoang', 'shabu', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,366 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'shabu', 'dunay', 'timbang', 'nga', 'singko', 'gramos', 'ug', 'nagkantidad', 'og', 'P25,000', '.'] 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, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,367 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Ramos', 'nasakpan', 'ng', 'daan', 'nga', 'nagsuyop', 'uban', 'sa', 'laing', 'lima', 'ka', '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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,368 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samdan', 'ang', '15', 'ka', 'tawo', 'human', 'magbangga', 'ang', 'usa', 'ka', 'bus', 'ug', '10-wheeler', 'truck', 'sa', 'highway', 'sakop', 'sa', 'Barangay', 'Pahug', 'ning', 'lungsod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0] | cebuaner |
5,369 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pag-abot', 'sa', 'nahitaboan', ',', 'mibalhin', 'sa', 'pikas', 'linya', 'ang', 'bus', 'gumikan', 'sa', 'naguba', 'nga', 'karsada', 'nunot', '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, 0, 0, 0] | cebuaner |
5,370 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakasugat', 'niini', 'ang', 'trak', 'nga', 'gimaneho', 'ni', 'Ramil', 'Yepes', 'nga', 'dunay', 'kargang', 'mga', 'bato', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0] | cebuaner |
5,371 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Misuway', 'pagpreno', 'si', 'Yepes', 'aron', 'malikayan', 'ang', 'panangbangga', 'apan', 'wa', 'kini', 'makakontrolar', 'gumikan', 'sa', 'basa', 'nga', 'karsada.', 'Samdan', 'ang', '13', 'ka', 'pasahero', 'sa', 'bus', 'ug', 'drayber', 'niini', 'ingon', 'man', 'ang', 'drayber', 'sa', 'trak', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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 |
5,372 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gidala', 'sa', 'Eastern', 'Visayas', 'Regional', 'Medical', 'Center', '(', 'EVRMC', ')', 'ang', 'mga', 'samdan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0] | cebuaner |
5,373 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daghang', 'mga', 'biyahe', 'ang', 'kanselado', 'samtang', 'liboan', 'na', 'usab', 'ka', 'mga', 'pasahero', 'ang', 'stranded', 'nga', 'gusto', 'unta', 'mouli', 'sa', 'ilang', 'probinsiya', 'karong', 'Pasko', 'tungod', 'sa', 'bagyong', 'Vinta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 8, 0] | cebuaner |
5,374 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Commander', 'Jerome', 'Cayabyab', 'sa', 'Cebu', 'Coast', 'Guard', 'nga', 'wa', 'nila', 'tugoti', 'ang', 'mga', 'sakyanan', 'sa', 'dagat', 'nga', 'mobiyahe', 'tungod', 'kay', 'ang', 'port', 'of', 'destinations', 'niini', 'gipaubos', 'sa', 'storm', 'signal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 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] | cebuaner |
5,375 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Samtang', 'niabot', 'sa', '1,531', 'ka', 'mga', 'pasahero', 'ang', 'stranded', 'alang', 'unta', 'sa', 'mga', 'biyahe', 'paingon', 'sa', 'Bohol', ',', 'Leyte', ',', 'Negros', 'Occidental', '/', 'Oriental', ',', 'Dipolog', ',', 'Surigao', ',', 'Cagayan', ',', 'ug', 'uban', 'pa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 6, 6, 6, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0] | cebuaner |
5,376 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'usay', 'mga', 'bus', 'nga', 'padulong', 'sa', 'Negros', 'Oriental', ',', 'labina', 'sa', 'dakbayan', 'sa', 'Dumaguate', 'ug', 'Mindanao', ',', 'ang', 'wa', 'usab', 'patabuka', 'gikan', '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, 5, 6, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0] | cebuaner |
5,377 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'ilang', 'forecast', ',', 'gilauman', 'nga', 'mo-landfall', 'ang', 'bagyong', 'Vinta', 'sa', 'Caraga', ',', 'Davao', 'Region', 'kagabii', 'o', 'karong', '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, 7, 8, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0] | cebuaner |
5,378 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Paingon', 'sa', 'kasadpan', 'ang', 'direksyon', 'sa', 'maong', 'bagyo', 'diin', 'aduna', 'pag-irog', 'nga', '21', 'kilometros', 'matag', 'takna', 'ug', 'nagdala', 'og', 'hangin', 'nga', '65', 'kilometros', 'matag', 'takna', 'ug', 'unos', 'nga', 'moabot', 'sa', '80', 'kilometros', 'matag', 'takna', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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] | cebuaner |
5,379 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Dugang', 'ni', 'Canasa', 'nga', 'susama', 'ra', 'ang', 'hangin', 'nga', 'gidala', 'ni', 'Vinta', 'ug', 'ni', 'Urduja', ',', 'apan', 'mas', 'paspas', 'ang', 'irog', 'ni', 'Vinta', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0] | cebuaner |
5,380 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'sa', 'labaw', 'sa', 'PDRRMO', 'Emergency', 'Response', 'ug', 'Search', 'and', 'Rescue', 'Division', ',', 'Kathy', 'dela', 'Calzada', ',', 'ang', 'mga', 'lungsod', 'sa', 'Malabuyoc', 'ug', 'Boljoon', ',', 'kinahanglang', 'magmabinantayon', 'human', 'nga', 'duna', 'nay', 'kasaysayan', 'sa', 'landslide', 'nga', 'mugna', 'sa', 'uwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 1, 2, 2, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,381 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'brgy.', 'Lower', 'Beceril', 'sa', 'lungsod', 'sa', 'Boljoon', ',', 'wa', 'pa', 'hingpit', 'nahupay', 'ang', 'kabalaka', 'sa', 'mga', 'residente', 'labot', 'sa', 'posibleng', 'pagdahili', 'na', 'usab', 'sa', 'yuta', 'dinhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 5, 6, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,382 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gawas', 'sa', 'mga', 'landslide', ',', 'biktima', 'sab', 'sa', 'mga', 'pagbaha', 'ang', 'daghang', 'dapit', 'sa', 'southern', 'Cebu', 'dihang', 'niigo', 'ang', 'mga', 'bagyong', 'Queenie', ',', 'Ruby', 'ug', 'Seniang', 'niadtong', '2014', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 0, 7, 8, 0, 7, 0, 7, 0, 0, 0] | cebuaner |
5,383 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihimug-atan', 'ni', 'dela', 'Calzada', 'ang', 'importansiya', 'sa', 'panag-alayon', 'sa', 'mga', 'residente', 'ngadto', 'na', 'sa', 'mga', 'barangay', 'o', 'kaha', 'municipal', 'ug', 'city', 'DRRM', 'aron', 'way', 'masinating', 'kakulian', ',', 'taliwala', '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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,384 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'pag-obserbar', 'sa', 'mga', 'lagda', 'sa', 'trapiko', 'di', 'lamang', 'makasiguro', 'sa', 'hapsay', 'nga', 'dagan', 'sa', 'trapiko', ',', 'makaluwas', 'usab', 'kini', 'sa', 'mga', 'kinabuhi', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,385 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'PNP', 'Traffic', 'Operation', 'Groups', 'sa', 'Cebu', 'City', 'Traffic', 'Office', '(', 'CCTO', ')', 'nitala', 'og', 'mas', 'daghang', 'mga', 'aksidente', 'karong', 'tuiga', ',', 'gikan', 'sa', 'Enero', 'ngadto', 'sa', 'Nobiyembre', ',', 'apan', 'mas', 'minos', 'ang', 'nangamatay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,386 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Base', 'sa', 'record', ',', 'dunay', '42', 'nga', 'nangamatay', 'sa', 'aksidente', 'sa', 'kadalanan', 'karong', 'tuiga', ',', 'mas', 'ubos', 'sa', '48', 'sa', '2016', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,387 | 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', 'dunay', 'tulo', 'ka', 'mga', 'tawo', 'ang', 'nangamatay', 'sa', 'aksidente', 'matag', 'buwan', 'sa', '2017', ',', 'mas', 'ubos', 'sa', '2016', 'nga', 'nag-average', 'og', 'upat', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,388 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'mas', 'daghan', 'ang', 'mga', 'aksidente', 'sa', '2017', ',', 'diin', 'niabot', 'og', '11,153', ',', 'mas', 'taas', 'itandi', 'sa', '10,918', 'sa', '2016', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,389 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpasabot', 'nga', 'dinhi', 'sa', 'dakbayan', 'sa', '2017', ',', 'dunay', 'labing', 'minos', '33', 'ka', 'mga', 'aksidente', 'sa', 'dalan', 'matag', 'adlaw', 'ang', 'mahitabo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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 |
5,390 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Sidney', 'Racaza', ',', 'control', 'operations', 'officer', ',', 'niingon', 'nga', 'kadaghanan', 'sa', 'mga', 'aksidente', 'sa', 'trapiko', 'naglambigit', 'og', 'motorsiklo', 'ug', 'way', 'pagtahod', 'sa', 'balaud', '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, 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] | cebuaner |
5,391 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Positibong', 'gitudlo', 'ni', 'Elizabeth', 'Ybañez', 'si', 'Tumara', 'nga', 'maoy', 'nipusil', 'patay', 'sa', 'iyang', 'bana', 'nga', 'si', 'konsehal', 'Iluminado', 'Ybañez', 'niadtong', '2013', 'sud', 'sa', 'ilang', 'balay', 'sa', 'Barangay', 'Maharuhay', '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, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 0] | cebuaner |
5,392 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Matod', 'ni', 'Elizabeth', 'nga', 'sa', 'wa', 'pa', 'pusila', 'ang', 'iyang', 'bana', ',', 'gipaarakan', 'pa', 'ang', 'ilang', 'balay', 'ni', 'Tumara', 'nga', 'niadtong', 'mga', 'tungora', 'way', 'tawo', 'nga', 'naigo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,393 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Apan', 'sa', 'Oktubre', 'sa', 'susamang', 'tuig', ',', 'dinhi', 'na', 'niya', 'gipatay', 'ang', 'konsehal', 'samtang', 'dunay', 'gihimong', 'pulong-pulong', 'sa', 'Barangay', 'Maharuhay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0] | cebuaner |
5,394 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sukad', 'niadto', 'wa', 'na', 'mopakita', 'si', 'Tumara', 'sa', 'ilang', 'lugar', 'ug', 'nisibat', 'bisan', 'pa', 'sa', 'ilang', 'paningkamot', 'nga', 'masikop', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,395 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakadawat', 'og', 'report', 'ang', 'PRO', '7', 'nga', 'naa', 'ra', 'sa', 'sitio', 'Taganas', ',', 'Barangay', 'San', 'Marcos', ',', 'lungsod', 'sa', 'Placer', 'nagtago', 'si', 'Tumara', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 5, 6, 6, 0, 0, 0, 5, 0, 0, 1, 0] | cebuaner |
5,396 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mopatuman', 'og', 'mga', 'kausaban', 'ang', 'Basilica', 'Minore', 'del', 'Sto.', 'Niño', 'atol', 'sa', 'ika-453', 'nga', 'kapistahan', 'sa', 'adlaw', 'nga', 'natawhan', 'sa', 'Balaang', 'Bata', 'nga', 'si', 'Sr.', 'Sto.', 'Niño', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0] | cebuaner |
5,397 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Magsugod', 'ang', 'nobenaryo', 'karong', 'Enero', '11', 'diin', 'ang', '“Walk', 'with', 'Jesus”', 'nga', 'manukad', 'sa', 'Fuente', 'Osmeña', 'magsugod', 'alas', '4', 'sa', 'kaadlawon', 'inay', 'alas', '4:30', 'sa', 'kaadlawon', 'ug', 'mao', 'sab', 'ang', 'sundon', 'sa', 'Walk', 'with', 'Mary', 'inig', 'Enero', '19', 'alang', 'sa', 'ika-9', 'nga', 'adlaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 6, 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] | cebuaner |
5,398 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Si', 'Fr.', 'Noel', 'Cogasa', 'nagkanayon', 'nga', 'gikan', 'sa', 'Enero', '12', 'hangtod', 'sa', '18', ',', 'adunay', 'tag', '11', 'ka', 'misa', 'ang', 'ipahigayon', 'sa', 'Basilica', 'nga', 'bahinon', 'sa', 'unom', 'ka', 'Bisaya', 'ug', '5', 'ka', 'English', 'nga', 'misa', 'nga', 'pagasugdan', 'alas', '4', 'sa', 'kadlawon', 'hangtod', 'alas', '7', 'sa', 'gabii', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories. | [0, 0, 1, 2, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
5,399 | What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Ang', 'Basilica', 'abli', '24', 'oras', 'para', 'sa', 'pag-ampo', 'apan', 'adunay', 'pipila', 'ka', 'oras', 'nga', 'sirad-an', 'kini', 'para', 'sa', 'paglimpyo', 'apan', 'ang', 'altar', 'diin', 'nahimutang', 'ang', 'Balaang', 'Bata', 'abli', '24', 'oras', 'para', 'sa', 'mga', 'tawo', 'nga', 'gustong', 'mobisita', 'ug', 'mohalok', 'sa', 'iyang', 'imahen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | cebuaner |
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