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4,300
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'talagsaong', 'kometa', 'nga', 'wala', 'makita', 'sa', '50,000', 'ka', 'tuig', 'ang', 'molabay', 'sa', 'Earth', ',', 'mao', 'kini', 'ang', '"', 'The', 'Green', 'Comet', '"', 'o', 'C', '/', '2022', 'E3', '(', 'ZTF', ')', '.', 'Ang', 'peak', 'niini', 'karong', 'Pebrero', '1-2', ',', '2023', 'human', 'sa', 'pagsalop', 'sa', 'adlaw', 'ug', 'sa', 'dili', 'pa', 'mosubang', 'ang', '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, 5, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,301
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LIBOAN', 'KA', 'MGA', 'TAMBAN', ',', 'NAANOD', 'SA', 'BAYBAYON', 'SA', 'HINOBA-AN', 'Nidagsa', 'sa', 'baybayon', 'ang', 'mga', 'residente', 'sa', 'Purok', '5', 'Taliptipon', ',', 'Culipapa', ',', 'Hinoba-an', 'niadtong', 'Biyernes', 'sa', 'gabii', ',', 'Enero', '27', ',', 'human', 'liboan', 'ka', 'mga', ''herring', ''', 'o', 'tamban', 'ang', 'naanod', 'sa', 'maong', 'baybayon.', 'Nakakolekta', 'og', 'mga', 'balde', 'nga', 'puno', 'sa', 'isda', 'ang', 'mga', 'nalipay', 'nga', 'lumolupyo', 'sa', 'naasoy', 'nga', '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, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,302
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SILLIMAN', 'UNIVERSITY', ',', 'GIILA', 'ISIP', 'NO.', '1', 'SCHOOL', 'SA', 'NURSING', 'EXAM', 'Nag-una', 'ang', 'Silliman', 'University', '(', 'SU', ')', 'sa', 'lista', 'sa', 'mga', 'nag-unang', 'eskwelahan', 'alang', 'sa', 'November', '2022', 'Nurse', 'Licensure', 'Examination', '(', 'NLE', ')', 'nga', 'aduna'y', '100', '%', 'passing', 'rate.', 'Tungod', 'niini', ',', 'nakadawat', 'og', 'pag-ila', 'ang', 'SU', 'gikan', 'sa', 'Professional', 'Regulation', 'Commission', '(', 'PRC', ')', '.', 'Gidawat', 'ni', 'Dr.', 'Theresa', 'A.', 'Guino-o', ',', 'SU', 'College', 'of', 'Nursing', 'dean', ',', 'ang', 'Certificate', 'of', 'Recognition', 'gikan', 'sa', 'PRC', 'sa', 'usa', 'ka', 'oathtaking', 'ceremony', 'alang', 'sa', 'mga', 'bag-ong', 'rehistrado', 'nga', 'mga', 'nurse', 'niadtong', 'Enero', '22', ',', '2023', 'sa', 'Philippine', 'International', 'Convention', 'Center', '(', 'PICC', ')', ',', 'Pasay', 'City.', 'Usa', 'sab', 'ang', 'SU', 'sa', '18', 'ka', 'mga', 'tunghaan', 'nga', 'nag-rank', '1', 'sa', 'lista', 'sa', 'Top-Performing', 'Schools', 'alang', 'sa', 'November', '2022', 'NLE.', 'Anaa', 'sab', 'tulo', 'ka', 'top-notchers', 'ang', 'SU', 'kinsa', 'ana', 'sa', 'rank', '5th', ',', '9th', ',', 'ug', '10th', 'sa', 'maong', 'exam.', 'Sa', 'niaging', 'tuig', ',', 'mao', 'ang', 'ikapitong', 'sunod-sunod', 'nga', 'tuig', 'nga', 'nakakuha', 'ang', 'SU', 'ug', '100', '%', 'passing', 'rate', 'sa', 'NLE.', 'Ang', 'SU', 'mao', 'ang', 'Center', 'of', 'Excellence', 'in', 'Nursing', 'Education', 'nga', 'gitudlo', 'sa', 'Commission', 'on', 'Higher', 'Education', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1, 2, 2, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 7, 0, 3, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 3, 4, 4, 4, 0]
|
cebuaner
|
4,303
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SALAGDOONG', 'BEACH', 'SA', 'SIQUIJOR', ',', 'ABLIHAN', 'NA', 'SA', 'PUBLIKO', 'HUMAN', 'ANG', 'DUL-AN', '3', 'KA', 'TUIG', 'Sa', 'pipila', 'ka', 'bulan', ',', 'abrihan', 'na', 'alang', 'sa', 'publiko', 'ang', 'Salagdoong', 'Beach', ',', 'usa', 'sa', 'mga', 'sikat', 'nga', 'tourist', 'attractions', 'sa', 'probinsya', 'sa', 'Siquijor.', 'Mao', 'kini', 'ang', 'gibutyag', 'ni', 'Governor', 'Jake', 'Vicente', 'S.', 'Villa', 'sa', 'Siquijor', 'sa', 'usa', 'ka', 'video', 'diin', 'aduna'y', 'sneak', 'peek', 'sa', 'maong', 'beach.', 'Mahinumduman', 'nga', 'gisira', 'og', 'dul-an', '3', 'ka', 'tuig', 'ang', 'Salagdoong', 'Beach', 'tungod', 'sa', 'pandemya', 'sa', '#', 'COVID19', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 6, 0, 5, 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 2, 2, 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, 0, 0, 0, 7, 0]
|
cebuaner
|
4,304
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PULIS', ',', 'GIDUNGGAB', 'PATAY', 'SA', 'AMLAN', 'Patay', 'ang', 'usa', 'ka', 'pulis', 'human', 'kini', 'gidunggab', 'sa', 'Barangay', 'Silab', 'sa', 'lungsod', 'sa', 'Amlan', 'ganinang', 'kadlawon', ',', 'Enero', '29', ',', '2023.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'P', '/', 'SSgt.', 'Rolando', 'Palic', ',', 'kinsa', 'nagpuyo', 'sa', 'naasoy', 'nga', 'barangay', 'ug', 'nakadestino', 'sa', 'Amlan', 'PNP.', 'Ang', 'suspek', 'giila', 'sa', 'kapulisan', 'nga', 'si', 'Jason', 'Cadiente', 'Ulpos', ',', 'lumulupyo', 'sab', 'sa', 'maong', 'dapit.', 'Nadala', 'pa', 'sa', 'tambalanaan', 'si', 'Palic', 'apan', 'nakabsan', 'kini', 'sa', 'iyang', 'kinabuhi', 'human', 'ang', 'pipila', 'ka', 'oras', ',', 'matud', 'sa', 'imbestigador', 'sa', 'insidente', 'nga', 'si', 'P', '/', 'CMSgt.', 'Nodielon', 'Dagodog.', 'Padayon', 'pa', 'ang', 'hot', 'pursuit', 'operation', 'sa', 'Amlan', 'PNP', 'aron', 'madakpan', 'ang', 'suspek', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0]
|
cebuaner
|
4,305
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PCHC', ':', 'MGA', 'TSEKE', 'KINAHANGLANG', 'ADUNAY', 'FULL', 'NUMERIC', 'ISSUE', 'DATES', 'SUGOD', 'SA', 'MAYO', 'Nagpahinumdom', 'sa', 'publiko', 'ang', 'Philippine', 'Clearing', 'House', 'Corporation', '(', 'PCHC', ')', 'nga', 'dili', 'na', 'dawaton', 'sugod', 'karong', 'Mayo', 'kadtong', 'mga', 'tseke', 'nga', 'aduna'y', 'issue', 'date', 'nga', 'gisulat', 'sa', 'alphanumeric', 'format', ',', 'gawas', 'sa', 'mga', 'pinili', 'nga', 'kaso.', 'Base', 'sa', 'Memorandum', 'Circular', '3738', 'niadtong', 'Enero', '17', ',', 'nagpahinumdom', 'ang', 'PCHC', 'sa', 'mga', 'clearing', 'participants', 'nga', 'kinahanglang', 'aduna'y', 'full', 'numeric', 'issue', 'date', 'ang', 'mga', 'tseke', 'sugod', 'sa', 'Mayo', '1', ',', 'ug', 'dawaton', 'lang', 'kadtong', 'mga', 'alphanumeric', 'issue', 'dates', 'hangtod', 'Abril', '30.', 'Niadtong', '2018', ',', 'gimando', 'sa', 'PCHC', 'nga', 'basahon', 'sa', 'clearing', 'banks', 'ang', 'mga', 'petsa', 'nga', 'gisulat', 'sa', 'numeric', 'format', 'sa', 'month-day-year', 'sequence', ',', 'nagpasabot', 'nga', 'kadtong', 'mga', 'tseke', 'sa', 'gipetsahan', 'og', 'Enero', '24', ',', '2024', 'kinahanglang', '01', '/', '24', '/', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,306
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GINABOT', 'SA', 'DUMAGUETE.', 'Dili', 'na', 'kinahanglan', 'pa', 'nga', 'mobiyahe', 'og', 'Cebu', 'aron', 'makatilaw', 'sa', 'lamian', 'nga', 'ginabot', ',', 'tungod', 'kay', 'kini', 'ania', 'na', 'sa', 'Dumaguete', '!', 'Sa', 'presyo', 'nga', 'P40', ',', 'makapalit', 'na', 'ka', 'sa', 'maong', 'ginabot', 'nga', 'anaa', 'makita', 'dapit', 'sa', 'St.', 'Paul', 'University', 'Dumaguete', 'sa', 'Barangay', 'Bantayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 5, 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, 5, 6, 6, 6, 0, 5, 6, 0]
|
cebuaner
|
4,307
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitay-og', 'sa', 'usa', 'ka', 'magnitude', '3.2', 'nga', 'linog', 'ang', 'pipila', 'ka', 'lugar', 'sa', 'Negros', 'Oriental', 'pasado', 'alas-3', 'karong', 'Huwebes', 'sa', 'hapon', ',', 'Enero', '26', ',', '2023.', 'Sumala', 'pa', 'sa', 'Phivolcs', ',', 'gisuta', 'niini', 'ang', 'epicenter', 'kon', 'tinubdan', 'sa', 'maong', 'linog', 'dapit', 'sa', 'lungsod', 'sa', 'Siaton.', 'Nabati', 'ang', 'Intensity', 'II', 'nga', 'pagtay-og', 'sa', 'lungsod', 'sa', 'Sibulan.', 'Hinuon', ',', 'walay', 'kadaot', 'ug', 'aftershocks', 'nga', 'gitan-aw', 'nga', 'posibleng', 'motumaw', 'tungod', 'sa', 'naasoy', 'nga', 'linog.', 'Beshie', ',', 'nakabati', 'ba', 'pud', 'ka', 'sa', 'linog', 'karong', 'hapon', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 0, 0, 0]
|
cebuaner
|
4,308
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['EKONOMIYA', 'SA', 'PILIPINAS', 'NISAKA', 'NGADTO', 'SA', '7.6', '%', 'NIADTONG', '2022', 'Paspas', 'nga', 'nisaka', 'ang', 'ekonomiya', 'sa', 'Pilipinas', 'nga', 'gipaabot', 'niadtong', '2022', ',', 'kini', 'human', 'nisaka', 'ngadto', 'sa', '7.2', '%', 'ang', 'gross', 'domestic', 'product', '(', 'GDP', ')', 'sa', 'fourth', 'quarter', 'niini.', 'Base', 'kini', 'sa', 'pasiuna', 'nga', 'datos', 'nga', 'gipagawas', 'sa', 'gobyerno', 'niadtong', 'Huwebes', ',', 'Enero', '26', ',', '2023.', 'Ang', '7.6', '%', 'nga', 'pagsaka', 'sa', 'GDP', 'alang', 'sa', 'maong', 'tuig', ',', 'nilapas', 'sa', '6.5-7.5', '%', 'nga', 'target', 'nga', 'gitakda', 'sa', 'economic', 'managers', ',', 'ug', 'ang', 'forecast', 'sa', 'median', 'analyst', 'nga', '6.8', '%', '.', 'Usa', 'sab', 'kini', 'sa', 'labing', 'paspas', 'nga', 'pagsaka', 'sa', 'GDP', 'sa', 'tibuok', 'kalibutang', ',', 'sumala', 'pa', 'sa', 'National', 'Economic', 'and', 'Development', 'Authority', '(', 'NEDA', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 4, 4, 4, 4, 4, 0]
|
cebuaner
|
4,309
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'klase', 'karon', 'sa', 'mga', 'mosunod', 'nga', 'lugar', 'sa', 'Negros', 'Oriental', 'karong', 'Huwebes', ',', 'Enero', '26', ',', '2023', ',', 'tungod', 'sa', 'pag-ulan', 'sa', 'probinsya', 'nga', 'dala', 'sa', 'low-pressure', 'area', 'ug', 'shear', 'line', ':'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,310
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PATAYNG', 'LAWAS', 'SA', 'LALAKI', ',', 'NAKIT-AN', 'SA', 'USA', 'KA', 'PAYAG', 'SA', 'DUMAGUETE', 'Usa', 'ka', 'patay', 'nga', 'lawas', 'ang', 'nakit-an', 'sa', 'Purok', 'Kanangkaan', ',', 'Bajumpandan', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'adlawa', ',', 'Enero', '25', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Jay-R', 'Kadusale', ',', '29', 'anyos', ',', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Dali', 'nga', 'niresponde', 'ang', 'kapulisan', 'sa', 'crime', 'scene', 'human', 'sila', 'makadawat', 'og', 'report', 'bahin', 'niini', 'ug', 'nakit-an', 'ang', 'patay', 'nga', 'lawas', 'sa', 'biktima', 'sulod', 'sa', 'usa', 'ka', 'payag.', 'Sa', 'pagkakaron', ',', 'padayon', 'pa', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'sa', 'maong', 'insidente', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,311
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['44', 'KA', 'MIYEMBRO', 'SA', 'SAF', ',', 'NAPATAY', 'SA', 'ENGKWENTRO', 'SA', 'MINDANAO', 'Karong', 'adlawa', 'niadtong', '2015', ',', 'nahitabo', 'ang', 'Mamasapano', 'massacre', 'diin', 'namatay', 'ang', '44', 'ka', 'mga', 'miyembro', 'sa', 'SAF', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,312
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['JOLLIBEE', 'BRANDS', ',', 'NALAKIP', 'SA', 'MGA', 'PINAKAPABORITONG', 'RESTAURANT', 'CHAINS', 'SA', 'U.S.', 'Nalakip', 'sa', 'lista', 'sa', 'America', ''s', 'Favorite', 'Restaurant', 'Chains', 'alang', 'sa', '2023', 'ang', 'Jollibee', ',', 'Chowking', ',', 'Smashburger', 'ug', 'The', 'Coffee', 'Bean', 'and', 'Tea', 'Leaf', '(', 'CBTL', ')', '.', 'Sumala', 'pa', 'sa', 'news', 'magazine', 'nga', 'Newsweek', 'ug', 'data', 'firm', 'nga', 'Statista.', 'Sa', 'usa', 'ka', 'pahayag', ',', 'niingon', 'ang', 'Jollibee', 'Foods', 'Corp', 'nga', 'gideterminar', 'ang', '2023', 'ranking', 'base', 'sa', 'resulta', 'sa', 'usa', 'ka', 'independent', 'survey', 'sa', 'kapin', '4,000', 'ka', 'mga', 'kustomer', 'ug', 'empleyado', 'sa', 'United', 'States.', 'Gipapili', 'ang', 'mga', 'respondent', 'kung', 'unsa', 'nga', 'mga', 'restaurant', 'ang', 'ilang', 'irekomenda', 'sa', '3', 'cuisines', 'ug', '13', 'dish', 'categories.', 'Ang', '3', 'ethnic', 'cuisines', 'mao', 'ang', 'Chinese', ',', 'Italian', 'ug', 'Tex-Mex', 'samtang', 'lakip', 'sa', '13', 'dish', 'categories', 'ang', 'BBQ', ',', 'burgers', ',', 'chicken', ',', 'coffee', '/', 'tea', '/', 'baked', 'goods', ',', 'ice', 'cream', '/', 'frozen', 'yogurt', ',', 'juice', 'ug', 'smoothies', ',', 'noodles', 'ug', 'ramen', ',', 'pizza', ',', 'sandwiches', ',', 'seafood', ',', 'soup', ',', 'steak', ',', 'ug', 'Sushi', ',', 'matod', 'pa', 'sa', 'JFC.', 'Napili', 'ang', 'Jollibee', 'sa', 'chicken', 'category', ',', 'Smashburger', 'sa', 'burgers', ',', 'CBTL', 'sa', 'tea', 'ug', 'coffee', ',', 'ug', 'Chowking', 'alang', 'sa', 'Chinese', 'cuisine', 'category.', 'Ilalom', 'sa', 'Jollibee', 'Group', 'umbrella', 'ang', '4', 'ka', 'mga', 'brands.', 'Nakuha', 'sa', 'JFC', 'ang', 'CBTL', 'niadtong', '2018.', 'Niadtong', 'Agosto', '2022', ',', 'ang', 'piniritong', 'manok', 'nga', 'mas', 'giilang', 'Chickenjoy', 'sa', 'maong', 'homegrown', 'brand', 'ang', 'giila', 'sab', 'isip', '"', 'best', 'fried', 'chicken', 'chain', 'in', 'America', '"', 'sa', 'usa', 'ka', 'food', 'website', 'nga', 'Eater', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 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, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,313
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P88', 'NGA', 'PLITE', 'HANGTOD', 'JAN.', '23', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'P88', 'nga', 'plite', 'hangtod', 'sa', 'Enero', '23', 'sa', 'mga', 'pili', 'nga', 'domestic', 'ug', 'internasyonal', 'nga', 'rota', 'sa', 'pagsaulog', 'sa', 'Chinese', 'New', 'Year.', 'Ilalom', 'sa', 'maong', 'seat', 'sale', ',', 'mahimong', 'makabiyahe', 'sa', 'nagkalain-laing', 'destinasyon.', 'Gikan', 'sa', 'Manila', ',', 'mahimong', 'makaadto', 'sa', 'Bacolod', ',', 'Boracay', ',', 'Bohol', ',', 'Puerto', 'Prinsesa', ',', 'Siargao', ',', 'ug', 'uban', 'pa.', 'Samtang', 'lakip', 'sa', 'international', 'spots', 'mao', 'ang', 'Tokyo', ',', 'Taipei', ',', 'Osaka', ',', 'Hong', 'Kong', ',', 'Bali', ',', 'Bangkok', ',', 'Dubai', ',', 'ug', 'uban', 'pa.', 'Aduna', 'kini', 'travel', 'period', 'gikan', 'sa', 'Setyembre', '1', 'hangtod', 'sa', 'Disyembre', '31', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,314
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unsa', 'man', 'ang', 'mahimong', 'kapalaran', 'sa', 'imong', 'love', 'life', 'karong', 'Year', 'of', 'the', 'Rabbit', '?', 'Single', 'pa', 'ba', 'gihapon', 'ka', '?', 'Magkauyab', 'na', 'ba', 'ka', '?', 'Magminyo', 'na', 'ba', '?', 'Sayri', 'dinhi', 'sa', 'imong', 'Chinese', 'romance', 'horoscope', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0]
|
cebuaner
|
4,315
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['USA', 'KA', 'TALAGSAONG', 'GREEN', 'COMET', ',', 'MOLABAY', 'SA', 'EARTH', 'KARONG', 'FEB.', '1', 'Usa', 'ka', 'talagsaong', 'berde', 'nga', 'kometa', 'ang', 'molabay', 'sa', 'Earth', 'karong', 'Pebrero', '1', ',', '2023.', 'Ulahi', 'kining', 'nakit-an', 'mga', '50,000', 'ka', 'tuig', 'na', 'ang', 'nilabay.', 'Ang', 'posibilidad', 'nga', 'makakita', 'ang', 'usa', 'ka', 'tawo', 'og', 'kometa', 'nagdepende', 'sa', 'pipila', 'ka', 'mga', 'sirkumstansya', ',', 'lakip', 'na', 'ang', 'lokasyon', 'ug', 'light', 'pollution', 'gikan', 'sa', 'natural', 'ug', 'hinimo', 'sa', 'tawo', 'nga', 'mga', 'tinubdan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 5, 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, 0, 0, 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
|
4,316
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PH', 'MO-IMPORT', 'OF', '450,000', 'MT', 'SA', 'KALAMAY', 'ALANG', 'SA', '2023', 'Nag-andam', 'na', 'ang', 'Sugar', 'Regulatory', 'Administration', '(', 'SRA', ')', 'aron', 'sa', 'pag-import', 'og', '450,000', 'metric', 'tons', 'nga', 'kalamay', 'karong', 'tuiga.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'mga', 'opisyales', 'niadtong', 'Miyerkules', ',', 'Enero', '18', ',', '2023.', 'Sumala', 'pa', 'ni', 'SRA', 'Board', 'Member', 'Pablo', 'Luis', 'Azcona', 'nga', 'bisan', 'paman', 'sa', 'taas', 'nga', 'lebel', 'sa', 'produksyon', 'sa', 'lokal', 'nga', 'kalamay', ',', 'dili', 'gihapon', 'kini', 'kaigo', 'aron', 'masustentuhan', 'ang', 'lokal', 'nga', 'panginahanglan.', 'Mas', 'taas', 'ang', 'gidaghanon', 'nga', 'ma-import', 'kung', 'itandi', 'sa', 'kinatibuk-ang', 'na-import', 'niadtong', '2022', ',', 'diin', 'naka-import', 'lamang', 'ang', 'nasud', 'og', '350,000', 'metric', 'tons', 'nga', 'duha', 'ka', 'order', 'sa', 'kalamay.', 'Dugang', 'pa', 'sa', 'SRA', ',', 'ganahan', 'sila', 'nga', 'aduna'y', 'buffer', 'stock', 'ang', 'nasud', 'nga', 'kaigo', 'sulod', 'sa', 'duha', 'ka', 'bulan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,317
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PINAKAGULANG', 'NGA', 'TAWO', 'SA', 'KALIBUTAN', ',', 'NAMATAY', 'NA', 'SA', 'EDAD', 'NGA', '118', 'Namatay', 'na', 'ang', 'French', 'nga', 'madre', 'nga', 'si', 'Lucile', 'Randon', ',', 'kinsa', 'giila', 'nga', 'pinakagulang', 'nga', 'tawo', 'sa', 'kalibutan', ',', 'sa', 'edad', 'nga', '118.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'tigpamaba', 'sa', 'Saint-Catherine-Labour', 'nursing', 'home', 'nga', 'si', 'David', 'Tavella', 'niadtong', 'Martes', ',', 'Enero', '17', ',', '2023.', 'Natawo', 'si', 'Randon', ',', 'kinsa', 'mas', 'nailhan', 'nga', 'Sister', 'Andre', ',', 'niadtong', 'Pebrero', '11', ',', '1904', 'sa', 'France.', 'Sumala', 'pa', 'ni', 'Tavella', ',', 'namatay', 'si', 'Randon', 'sa', 'iyang', 'pagkatulog', 'sa', 'nursing', 'home', 'sa', 'Toulon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 7, 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, 5, 6, 6, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,318
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AIRASIA', 'NAGTANYAG', 'OG', 'P71', 'NGA', 'PLITE', 'HANGTOD', 'SA', 'ENERO', '29', 'Nagtanyag', 'ang', 'AirAsia', 'og', 'seat', 'sale', 'nga', 'P71', 'hangtod', 'sa', 'Enero', '29', ',', '2023.', 'Ilalom', 'sa', 'maong', 'promo', ',', 'mahimo', 'nakang', 'mobiyahe', 'sa', 'nga', 'local', 'spots', 'sa', 'Pilipinas', 'sama', 'sa', 'Cebu', ',', 'Kalibo', ',', 'ug', 'Iloilo', 'gikan', 'sa', 'Manila.', 'Samtang', 'gikan', 'sa', 'Cebu', ',', 'mahimo', 'kang', 'mobiyahe', 'sa', 'mga', 'domestic', 'destinations', 'sama', 'sa', 'Caticlan', ',', 'Cagayan', 'de', 'Oro', ',', 'ug', 'Davao.', 'Ang', 'booking', 'period', 'gikan', 'sa', 'Enero', '16-29', ',', 'samtang', 'ang', 'travel', 'period', 'gikan', 'sa', 'Enero', '16', 'hangtod', 'sa', 'Hulyo', '31.', 'Mahimong', 'mo-sign', 'up', 'alang', 'sa', 'usa', 'ka', 'membership', 'sa', 'AirAsia', 'Super', 'App', 'gamit', 'ang', 'imong', 'mobile', 'phone', 'ug', 'paghimo', 'og', 'account', 'pinaagi', 'sa', 'Gmail.', 'Gikan', 'niana', ',', 'mahimong', 'magsugod', 'na', 'sa', 'pag-book', 'sa', 'imong', 'biyahe', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 6, 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,319
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DOMESTIC', 'AIRLINES', 'PAUBSAN', 'ANG', 'PLITE', 'KARONG', 'PEBRERO', 'Gikompirmar', 'sa', 'domestic', 'airlines', 'nga', 'Philippine', 'Airlines', ',', 'Cebu', 'Pacific', 'ug', 'AirAsia', 'Philippines', 'nga', 'ilang', 'paubsan', 'ang', 'plite', 'sa', 'Pebrero', 'subay', 'sa', 'mando', 'nga', 'minusan', 'ang', 'fuel', 'surcharge', 'alang', 'sa', 'maong', 'bulan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 0, 3, 4, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,320
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BATAN-ON', ',', 'PATAY', 'HUMAN', 'NASUYOP', 'SA', 'TAMBURONG', 'SA', 'PAMPLONA', 'Usa', 'ka', 'batan-on', 'ang', 'namatay', 'human', 'nasuyop', 'sa', 'tamburong', 'ug', 'nalumos', 'sa', 'Sta.', 'Agueda', ',', 'Pamplona', 'niadtong', 'Dominggo', ',', 'Enero', '15', ',', '2023.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'John', 'Jameson', 'Sarte', ',', '18', 'anyos', ',', 'ug', 'lumolupyo', 'sa', 'Upper', 'Buntis', ',', 'Bacong.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'MDRRMO', 'sa', 'Pamplona', ',', 'naligo', 'sa', 'maong', 'suba', 'ang', 'grupo', 'ni', 'Sarte.', 'Wala', 'damha', 'sa', 'biktima', 'nga', 'naglangoy', 'siya', 'duol', 'sa', 'tamburong', 'diin', 'aduna'y', 'daghang', 'bato', 'ug', 'kahoy', 'nga', 'nakabara', 'sa', 'lungag', 'niini.', 'Gisuyop', 'sa', 'tubig', 'ang', 'biktima', 'ug', 'nisulay', 'paglangoy', 'apan', 'napakyas', 'kini', 'tungod', 'sa', 'kakusog', 'sa', 'tubig.', 'Nadugayan', 'pagkuha', 'ang', 'biktima.', 'Nangita', 'sab', 'og', 'mga', 'pamaagi', 'ang', 'mga', 'awtoridad', 'aron', 'makuha', 'ang', 'lawas', 'ni', 'Sarte.', 'Pagkuha', 'sa', 'lawas', 'sa', 'biktima', ',', 'gisulayan', 'pa', 'kini', 'pag-revive', 'ug', 'gidala', 'sa', 'usa', 'ka', 'klinika', 'apan', 'gideklara', 'kining', 'patay', '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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,321
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PINASKUHAN', 'ALANG', 'SA', '34K', 'KA', 'SENIOR', 'CITIZEN', ',', 'PWD', ',', 'UG', 'PAMILYANG', 'KABUS', 'SA', 'DUMAGUETE', ',', 'I-APOD-APOD', 'KARONG', 'SEMANA', 'Iapod-apod', 'na', 'ang', 'Handog', 'Pamasko', 'alang', 'sa', '30', 'ka', 'barangay', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'karong', 'Enero', '18', '(', 'Miyerkules', ')', 'ug', 'Enero', '19', '(', 'Huwebes', ')', '.', 'Gipagawas', 'sa', 'City', 'Social', 'Welfare', 'and', 'Development', 'Office', 'ang', 'schedule', 'sa', 'pag-apod-apod', 'niini', 'human', 'makompleto', 'ang', 'pag-verify', 'sa', '34,200', 'ka', 'mga', 'benepisyaryo.', 'Gibuyag', 'ni', 'Mayor', 'Felipe', 'Remollo', 'nga', 'nigahin', 'ang', 'City', 'Government', 'og', 'P17.1', 'milyon', 'nga', 'pondo', 'alang', 'sa', 'maong', 'programa', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,322
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PBBM', ':', 'PILIPINAS', 'KINAHANGLAN', 'NANG', 'MOANGKAT', 'OG', 'SIBUYAS', 'TUNGOD', 'KAY', 'KULANG', 'ANG', 'LOKAL', 'NGA', 'ANI', 'Gibutyag', 'ni', 'President', 'Ferdinand', 'Marcos', 'Jr.', 'niadtong', 'Dominggo', 'nga', 'napugos', 'ang', 'gobyerno', 'sa', 'Pilipinas', 'sa', 'pag-import', 'og', 'sibuyas', 'tungod', 'sa', 'kakulangon', 'sa', 'lokal', 'nga', 'suplay', 'ug', 'taas', 'nga', 'demand', 'niini.', 'Bag-ohay', 'lamang', ',', 'gianunsyo', 'sa', 'agricultural', 'department', 'nga', 'mopatuman', 'sila', 'og', '"', 'calibrated', 'importation', '"', 'sa', 'mga', 'sibuyas', 'bisan', 'paman', 'sa', 'umalabot', 'nga', 'pag-ani.', 'Ang', 'maba', 'nga', 'suplay', 'niini', 'mao'y', 'hinungdan', 'sa', 'pagsaka', 'sa', 'presyo', 'sa', 'mga', 'utanon', 'sa', 'mga', 'niaging', 'semana.', 'Apan', 'pipila', 'ka', 'mga', 'grupo', 'ang', 'nipadayag', 'sa', 'ilang', 'kabalaka', 'nga', 'ang', 'pag-import', 'mahimong', 'makaapekto', 'sa', 'mga', 'lokal', 'nga', 'mag-uuma', 'sa', 'sibuyas.', 'Gisubli', 'ni', 'Marcos', ',', 'mopatuman', 'ang', 'iyang', 'administrasyon', 'og', 'mga', 'estratehiya', 'aron', 'pagdugang', 'sa', 'lokal', 'nga', 'produksyon', 'sa', 'sibuyas', 'ug', 'kalamay', ',', 'aron', 'mawala', 'ang', 'panginahanglan', 'sa', 'pag-import', 'nga', 'makapasaka', 'sa', 'risgo', 'sa', 'inflation', '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.
|
[1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,323
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAULANON', 'NGA', 'WEEKEND', ',', 'GILAOMAN', 'TUNGOD', 'SA', 'LPA', 'Magpadayon', 'ang', 'pag-ulan', 'sa', 'pipila', 'ka', 'parte', 'sa', 'nasud', 'karong', 'weekend', 'tungod', 'sa', 'low', 'pressure', 'area', '(', 'LPA', ')', 'ug', 'northeast', 'monsoon', 'o', 'amihan.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'PAGASA', 'karong', 'adlawa', ',', 'Enero', '14', ',', '2023.', 'Sumala', 'pa', 'sa', 'weather', 'state', 'bureau', ',', 'anaa', 'ang', 'LPA', 'sa', 'baybayon', 'sa', 'Lanuza', ',', 'Surigao', 'del', 'Sur', 'mga', 'alas-3', 'sa', 'kadlawon.', 'Magdala', 'kini', 'og', 'katag-katag', 'ngadto', 'sa', 'lapad', 'nga', 'pag-ulan', 'ug', 'pagdalugdog', 'sa', 'Bicol', 'Region', ',', 'Eastern', 'Visayas', ',', 'Dinagat', 'Islands', ',', 'ug', 'Surigao', 'del', 'Norte.', 'Matod', 'kini', 'sa', 'gipagawas', 'nga', '24-hour', 'weather', 'bulletin', 'sa', 'maong', 'ahensya', 'mga', 'alas-4', 'sa', 'kadlawon', 'karong', 'adlawa.', 'Dugang', 'pa', 'nila', ',', 'gilaoman', 'nga', 'makasinati', 'og', 'katag-katag', 'nga', 'pag-ulan', 'ug', 'pagdalugdog', 'ang', 'Calabarzon', ',', 'Mimaropa', ',', 'ug', 'ubang', 'parte', 'sa', 'Visayas', 'ug', 'Mindanao', 'tungod', 'sa', 'LPA', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0]
|
cebuaner
|
4,324
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'hulagway', 'sa', 'usa', 'ka', 'batang', 'babayi', 'nga', 'nakakuha', 'sa', 'kasingkasing', 'sa', 'mga', 'netizens', 'diin', 'nag-costume', 'kini', 'og', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
|
cebuaner
|
4,325
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Bes', ',', 'kung', 'ikaw', 'ang', 'pangutan-on', ',', 'malas', 'ba', 'ang', '#', 'FridayThe13th', '?', 'Giingong', 'malas', 'ang', '13', 'tungod', 'nagsunod', 'kini', 'sa', 'number', '12', ',', 'nga', 'gikonsiderar', 'isip', '"', 'complete', 'number', '"', '--', 'sama', 'sa', '12', 'ka', 'bulan', 'sa', 'usa', 'ka', 'tuig', ',', '12', 'ka', 'signs', 'sa', 'zodiac', ',', '12', 'ka', 'gods', 'sa', 'Olympus', ',', 'ug', '12', 'ka', 'apostoles', 'ni', 'Jesus', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 0]
|
cebuaner
|
4,326
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'tindahan', 'sa', 'Dao', 'Public', 'Market', 'sa', 'Tagbilaran', 'City', ',', 'Bohol', 'ang', 'nag-promo', 'pinaagi', 'sa', 'pagbayad', 'sa', 'GCash', ',', 'sama', 'sa', 'P1', 'kada', 'itlog', 'ug', 'P20', 'kada', 'kilo', 'sa', 'bugas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,327
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Girampa', 'ni', 'Celeste', 'Cortesi', 'ang', 'iyang', 'national', 'costume', 'atol', 'sa', 'preliminary', 'competition', 'sa', '#', '71stMissUniverse', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0]
|
cebuaner
|
4,328
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAUDNON', 'PAGBALIK', 'SA', 'NEGROS', 'ISLAND', 'REGION', ',', 'GIAPRUBAHAN', 'NA', 'SA', 'USA', 'KA', 'PANEL', 'SA', 'KAMARA', 'Giaprobahan', 'sa', 'House', 'committee', 'sa', 'local', 'government', 'niadtong', 'Martes', 'ang', 'usa', 'ka', 'substitute', 'bill', 'nga', 'maghimo', 'sa', 'Negros', 'Island', 'Region.', 'Ilalom', 'sa', 'maong', 'proposal', ',', 'maglangkob', 'sa', 'usa', 'ka', 'rehiyon', 'ang', 'Negros', 'Occidental', ',', 'Negros', 'Oriental', 'ug', 'Siquijor.', 'Sumala', 'pa', 'ni', 'Negros', 'Occidental', 'sixth', 'district', 'Rep.', 'Mercedes', 'Alvarez', ',', 'gipadayag', 'kaniadto', 'nila', 'ni', 'Siquijor', 'Gov.', 'Jake', 'Vincent', 'Villa', 'ug', 'Siquijor', 'Rep.', 'Zaldy', 'Villa', 'sa', 'atubangan', 'sa', 'technical', 'working', 'group', 'sa', 'House', 'panel', 'ang', 'paglakip', 'sa', 'Siquijor', 'sa', 'rehiyon.', 'Gibutyag', 'ni', 'Negros', 'Occidental', 'Gov.', 'Eugenio', 'Jose', 'Lacson', 'nga', 'plano', 'niya', 'nga', 'makigkita', 'ni', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'sunod', 'semana', 'aron', 'hisgutan', 'ang', 'gisugyot', 'nga', 'paghiusa', 'sa', 'duha', 'ka', 'probinsya.', 'Una', 'ng', 'gipahayag', 'ni', 'Degamo', 'ang', 'iyang', 'pagsupak', 'sa', 'paghimo', 'sa', 'maong', 'rehiyon', ',', 'tungod', 'sa', 'giingong', '"', 'cultural', 'differences”', 'tali', 'sa', 'duha', 'ka', 'probinsya', 'sa', 'Negros.', 'Matod', 'pa', 'ni', 'Alvarez', 'nga', 'wala'y', 'nadawat', 'ang', 'House', 'panel', 'og', 'position', 'paper', 'gikan', 'ni', 'Degamo.', 'Sa', 'dihang', 'gipangutana', 'si', 'Alvarez', 'kung', 'ang', 'pagsupak', 'ni', 'Degamo', 'mahimo', 'bang', 'makahunong', 'sa', 'paghimo', 'sa', 'rehiyon', ',', 'gitubag', 'kini', 'niya', 'nga', 'anaa', 'sa', 'Kongreso', 'ang', 'pag-aprobar', 'sa', 'maong', 'balaudnon.', 'Gihimo', 'ang', 'Negros', 'Island', 'Region', 'ilalom', 'sa', 'executive', 'order', 'ni', 'kanhi', 'presidente', 'Benigno', 'Aquino', 'III', 'niadtong', '2015.', 'Apan', 'giwagtang', 'kini', 'sigon', 'sa', 'mando', 'ni', 'kanhi', 'presidente', 'Rodrigo', 'Duterte', 'niadtong', '2017', 'tungod', 'sa', 'kakulangon', 'sa', 'budget', 'alang', 'sa', 'maong', 'rehiyon', '#', 'NewsBite', '|', 'with', 'reports', 'from', 'The', 'Philippine', 'Star'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 0, 0, 0, 5, 6, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 5, 0, 1, 2, 2, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 6, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 3, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4]
|
cebuaner
|
4,329
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Girampa', 'ni', 'Celeste', 'Cortesi', 'ang', 'iyang', 'evening', 'gown', 'atol', 'sa', 'preliminary', 'competition', 'sa', '#', '71stMissUniverse.', 'Si', 'Cortesi', 'ang', 'pambato', 'sa', 'Pilipinas', 'sa', 'maong', 'pageant', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0]
|
cebuaner
|
4,330
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['COVID-19', 'EMERGENCY', ',', 'POSIBLENG', 'MATAPOS', 'NA', 'KARONG', '2023', ',', 'MATUD', 'PA', 'SA', 'WHO', 'Gipanan-aw', 'sa', 'World', 'Health', 'Organization', '(', 'WHO', ')', 'ang', 'pagtangtang', 'sa', 'deklarasyon', 'sa', 'sakit', 'nga', 'Covid-19', 'isip', 'public', 'health', 'emergency', 'karong', 'tuiga.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'usa', 'ka', 'opisyal', 'niadtong', 'Miyerkules', ',', 'Enero', '11', ',', '2023.', 'Gideklarar', 'sa', 'WHO', 'ang', 'kaniadto', 'novel', 'coronavirus', 'outbreak', 'isip', 'public', 'health', 'emergency', 'nga', 'aduna'y', 'International', 'concern', 'niadtong', 'Enero', '30', ',', '2020', ',', 'ug', 'gideklarar', 'isip', 'pandemya', 'niadtong', 'Marso', '11', 'sa', 'naasoy', 'nga', 'tuig.', 'Gisubli', 'ni', 'Kerkhove', 'nga', 'bisan', 'paman', 'sa', 'nagpadayong', 'viral', 'transmission', 'sa', 'tibuok', 'kalibutan', ',', 'aduna', 'na'y', 'mga', 'gamit', 'ang', 'tanang', 'nasud', 'aron', 'madumala', 'ang', 'grabe', 'nga', 'mga', 'kaso', 'sa', 'sakit', 'ug', 'kamatayon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,331
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AMPING', 'GIHAPON', 'SA', 'ULAN', ',', 'BESHIE', '!', 'Giisa', 'sa', 'PAGASA', 'ang', 'ORANGE', 'RAINFALL', 'WARNING', 'sa', 'Negros', 'Oriental', 'ug', 'Siquijor', 'karong', 'adlawa', ',', 'Enero', '12', ',', '2023', ',', 'tungod', 'sa', 'walay', 'hunong', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'low-pressure', 'area', '(', 'LPA', ')', 'ug', 'shear', 'line.', 'Tungod', 'niini', ',', 'gipahimangnuan', 'ang', 'tanang', 'mga', 'Negrense', 'ug', 'Siquijodnon', 'nga', 'magmatngon', 'gihapon', 'sa', 'mga', 'posibleng', 'flash', 'flood', 'o', 'landslide', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 6, 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, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,332
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Beshie', ',', 'ania', 'ang', 'mga', 'emergency', 'numbers', 'alang', 'sa', 'mga', 'lokal', 'nga', 'awtoridad', 'ning', 'dakbayan', 'sa', 'Dumaguete.', 'Karong', 'walay', 'hunong', 'ang', 'pag-ulan', ',', 'save', 'these', 'digits', 'in', 'case', 'of', 'emergency.', 'Amping', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 0, 0, 0, 0, 0]
|
cebuaner
|
4,333
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Suspendido', 'na', 'ang', 'klase', 'sa', 'tanang', 'lebel', 'sa', 'tanang', 'pampubliko', 'ug', 'pribadong', 'eskuwelahan', 'sa', 'tibuok', 'DUMAGUETE', 'CITY', 'karong', 'Huwebes', ',', 'Enero', '12', ',', '2023.', 'Kini', 'tungod', 'gihapon', 'sa', 'walay', 'hunong', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'low-pressure', 'area', 'ug', 'shear', 'line.', 'Ang', 'maong', 'pagsuspenso', ',', 'gimando', 'ni', 'Mayor', 'Felipe', 'Remollo', 'kagabii', ',', 'Enero', '11', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0]
|
cebuaner
|
4,334
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gisuspinde', 'na', 'ang', 'klase', 'sa', 'tanang', 'lebel', 'sa', 'tanang', 'pampubliko', 'ug', 'pribadong', 'tunghaan', 'sa', 'tibuok', 'Negros', 'Oriental', 'karong', 'Huwebes', ',', 'Enero', '12', ',', '2023', ',', 'taliwala', 'sa', 'walay', 'hunong', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'low-pressure', 'area', 'ug', 'shear', 'line.', 'Kini', 'sigon', 'sa', 'anunsyo', 'ni', 'Gov.', 'Roel', 'Degamo', 'sa', 'iyang', 'Facebook', 'page', 'karong', 'gabii', ',', 'Enero', '11', ',', '2023.', 'Magpabiling', 'suspendido', 'ang', 'mga', 'klase', '"', 'until', 'further', 'notice', ',', '"', 'o', 'kun', 'dunay', 'laing', 'anunsyo', 'pagpabalik', 'sa', 'klase', 'sa', 'mga', 'mosunod', '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, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,335
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DOBLE', 'AMPING', 'SA', 'ULAN', ',', 'BESHIE', '!', 'Giisa', 'na', 'sa', 'PAGASA', 'ang', 'kinatas-an', 'nga', 'RED', 'RAINFALL', 'WARNING', 'sa', 'Negros', 'Oriental', 'karong', 'gabii', ',', 'Enero', '11', ',', '2023', ',', 'tungod', 'sa', 'padayon', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'low-pressure', 'area', '(', 'LPA', ')', 'ug', 'shear', 'line.', 'Tungod', 'niini', ',', 'gipahimangnuan', 'ang', 'tanan', 'nga', 'magmatngon', 'sa', 'seryosong', 'pagbaha', 'ug', 'pagdahili', 'sa', 'yuta', 'kun', 'landslide', ',', 'ilabi', 'na', 'sa', 'mga', 'nagpuyo', 'sa', 'mga', 'bukirang', 'lugar.', 'Gawas', 'sa', 'Negros', 'Oriental', ',', 'gipaubos', 'na', 'sab', 'sa', 'red', 'rainfall', 'warning', 'ang', 'mga', 'lalawigan', 'sa', 'Eastern', 'Samar', ',', 'Samar', ',', 'Biliran', ',', 'ug', 'Leyte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 5, 0, 0, 5, 0]
|
cebuaner
|
4,336
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gipaubos', 'na', 'sa', 'PAGASA', 'ang', 'tibuok', 'Negros', 'Oriental', 'sa', 'ORANGE', 'RAINFALL', 'WARNING', 'tungod', 'sa', 'epekto', 'sa', 'low-pressure', 'area', 'ug', 'shear', 'line.', 'Gipahimangno', 'ang', 'tanan', 'nga', 'magmatngon', 'sa', 'posibleng', 'kalit', 'nga', 'pagbaha', 'ug', 'landslides', 'kun', 'pagdahili', 'sa', 'yuta', 'tungod', 'sa', 'kusog', 'ug', 'walay', 'hunojg', 'nga', 'pag-ulan.', 'Gipaubos', 'sab', 'sa', 'orange', 'rainfall', 'warning', 'ang', 'amihanang', 'Sugbo', 'ug', 'Southern', 'Leyte', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0]
|
cebuaner
|
4,337
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SIDLAKAN', 'DANCE', 'COMPANY', 'SA', 'NEGOR', ',', 'MOSALMOT', 'SA', 'FIDAF', 'BRAZIL', 'WORLD', 'CHAMPIONSHIP', '2023', 'Ang', 'Sidlakan', 'Dance', 'Company', '(', 'SDC', ')', 'mao', 'ang', 'opisyal', 'nga', 'delegado', 'sa', 'Pilipinas', 'alang', 'sa', '19th', 'Nova', 'Prata', 'International', 'Folk', 'Festival', ',', 'FIDAF', 'Brazil', 'World', 'Championship', '2023', 'sa', 'Brazil', 'karong', 'Setyembre.', 'Sila', 'ang', 'bugtong', 'representante', 'sa', 'Pilipinas', 'ug', 'makigkompetensya', 'sa', '9', 'ka', 'nagkalain-laing', 'mga', 'nasud', 'alang', 'sa', 'world', 'championship', 'title.', 'Ang', 'SDC', 'mao', 'ang', 'nakakuha', 'sa', 'World', 'Grand', 'Champion', 'award', 'atol', 'sa', '2018', 'IYF', 'International', 'Cultural', 'Dance', 'Festival', 'and', 'Championship', 'sa', 'South', 'Korea.', 'Niadtong', '2019', ',', 'girepresentar', 'nila', 'ang', 'Pilipinas', 'sa', '2019', 'Horticultural', 'Exposition', 'sa', 'Beijing', 'China', 'nga', 'gitawag', 'isip', 'Flower', 'Festival', 'of', 'the', 'World.', 'Gilangkuban', 'ang', 'delagado', 'sa', 'mga', 'estudyante', 'ug', 'mga', 'average-earning', 'nga', 'mga', 'indibidwal', 'nga', 'aduna'y', 'paglaom', 'nga', 'mapasigarbuhon', 'nga', 'ipataas', 'ang', 'bandila', 'sa', 'Pilipinas.', 'Nanghinaot', 'sila', 'nga', 'makapangayo', 'og', 'pinansyal', 'nga', 'suporta', 'aron', 'makatabang', 'sa', 'ilang', 'airfare', 'expenses.', 'Bisan', 'pila', 'nga', 'kantidad', ',', 'dako', 'na', 'kaayo', 'og', 'tabang', 'nila.', 'Mahimong', 'i-contact', 'si', 'Vic', 'Nocete.', 'Bes', ',', 'atong', 'suportahan', 'ang', 'SDC', '!', '❤️'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 4, 0, 5, 0, 0, 0, 7, 8, 8, 8, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 7, 8, 8, 8, 8, 0, 7, 8, 8, 8, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 5, 6, 0, 0, 0, 0, 0, 0, 5, 0, 0, 7, 8, 0, 5, 6, 0, 0, 0, 7, 8, 8, 8, 8, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 3, 0, 0]
|
cebuaner
|
4,338
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giisa', 'sa', 'PAGASA', 'ang', 'yellow', 'heavy', 'rainfall', 'warning', 'sa', 'mga', 'lalawigan', 'sa', 'Siquijor', 'ug', 'Negros', 'Oriental', ',', 'lakip', 'na', 'ang', 'Dumaguete', 'City', ',', 'Kini', 'tungod', 'gihapon', 'sa', 'kusog', 'nga', 'pag-ulan', 'nga', 'dala', 'sa', 'shear', 'line', 'ug', 'sa', 'low-pressure', 'area', '(', 'LPA', ')', 'nga', 'anaa', 'sa', 'sidlakang', 'bahin', 'sa', 'Mindanao.', 'Tungod', 'niini', ',', 'gipahimangno', 'ang', 'mga', 'lumolupyo', 'sa', 'maong', 'mga', 'dapit', 'nga', 'magmatngon', 'sa', 'posibleng', 'flashflood', 'kun', 'kalit', 'nga', 'pagbaha', 'ug', 'mga', 'landslide', 'kun', 'pagdahili', 'sa', 'yuta', ',', 'ilabi', 'na', 'sa', 'mga', 'bukirang', '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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]
|
cebuaner
|
4,339
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LPA', ',', 'SHEAR', 'LINE', 'MAGDALA', 'OG', 'PAG-ULAN', 'SA', 'KABISAY-AN', 'Sa', 'mosunod', 'nga', '24', 'ka', 'oras', ',', 'magdala', 'ang', 'Low', 'Pressure', 'Area', '(', 'LPA', ')', 'ug', 'Shear', 'Line', 'og', 'kasarangan', 'ngadto', 'sa', 'kusog', 'nga', 'pag-ulan', 'sa', 'Eastern', 'Visayas', ',', 'Central', 'Visayas', ',', 'Zamboanga', 'Peninsula', ',', 'Dinagat', 'Islands', ',', 'Surigao', 'del', 'Norte', 'ug', 'Surigao', 'del', 'Sur.', 'Samtang', ',', 'aduna'y', 'hinay', 'ngadto', 'sa', 'kasarangan', 'ug', 'usahay', 'kusog', 'nga', 'pag-ulan', 'BARMM', ',', 'Northern', 'Mindanao', ',', 'Bicol', 'Region', 'ug', 'ubang', 'bahin', 'sa', 'Caraga', 'ug', 'Visayas.', 'Gibantayan', 'karon', 'ang', 'usa', 'ka', 'LPA', 'nga', 'anaa', 'sa', 'gibanabanang', '575', 'km', 'sa', 'silangang', 'bahin', 'sa', 'Surigao', 'City', ',', 'Surigao', 'del', 'Norte', 'mga', 'alas-10', 'sa', 'buntag', 'karong', 'adlawa', ',', 'Enero', '11', ',', '2023.', 'Aduna', 'kini', 'posibilidad', 'nga', 'mahimong', 'tropical', 'depression', 'sulod', 'sa', 'mosunod', 'nga', '24', 'ka', 'oras', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 5, 6, 6, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,340
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gilugwayan', 'sa', 'Miss', 'Universe', 'Philippines', 'ang', 'deadline', 'sa', 'aplikasyon', 'alang', 'sa', 'maong', 'pageant', 'karong', '2023.', 'Gianunsyo', 'sa', 'organisasyon', 'nga', 'mahimong', 'mosumite', 'sa', 'mga', 'aplikasyon', 'hangtod', '11:59pm', 'sa', 'Pebrero', '5.', 'Kaniadto', ',', 'gitakda', 'sa', 'Miss', 'Universe', 'Philippines', 'ang', 'deadline', 'sa', 'mga', 'aplikasyon', 'sa', '11:59pm', 'sa', 'Enero', '29.', 'Gibag-o', 'sab', 'ang', 'pipila', 'ka', 'mga', 'requirements', 'sa', 'Miss', 'Universe', 'Organization', 'aron', 'tugutan', 'nga', 'makaapil', 'sa', 'internasyonal', 'nga', 'kompetisyon', 'ang', 'mga', 'inahan', 'ug', 'kadtong', 'mga', 'minyo', 'na.', 'Ang', 'modaog', 'sa', 'pageants', 'sa', '2023', 'mao'y', 'mosunod', 'ni', 'Celeste', 'Cortesi', ',', 'kinsa', 'gitakda', 'nga', 'mosalmot', 'sa', '71st', 'Miss', 'Universe', 'sa', 'New', 'Orleans', ',', 'Louisiana', 'sa', 'United', 'States', 'karong', 'Enero', '14', '(', 'Enero', '15', 'sa', 'Pilipinas', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 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, 0, 0, 0, 0, 0, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 5, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0]
|
cebuaner
|
4,341
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LPA', 'MAGPAULAN', 'SA', 'NEGROS', 'ORIENTAL', 'UG', 'KABISAY-AN', 'Gibantayan', 'ang', 'usa', 'ka', 'low', 'pressure', 'area', '(', 'LPA', ')', 'nga', 'anaa', 'sa', '425', 'km', 'sa', 'silangang', 'bahin', 'sa', 'Hinatuan', ',', 'Surigao', 'del', 'Sur', 'mga', 'alas-10', 'sa', 'buntag', 'karong', 'adlawa', ',', 'ug', 'aduna'y', 'posibilidad', 'nga', 'mahimong', 'tropical', 'depression', 'sulod', 'sa', '24', 'ka', 'oras.', 'Base', 'kini', 'sa', 'datos', 'nga', 'gipagawas', 'sa', 'DOST-PAGASA', 'mga', 'alas-11', 'sa', 'buntag', 'karong', 'adlawa', ',', 'Enero', '10', ',', '2023.', 'Magdala', 'ang', 'LPA', 'og', 'kasarangan', 'ngadto', 'sa', 'kusog', 'nga', 'pag-ulan', 'sa', 'Sorsogon', ',', 'Masbate', ',', 'Eastern', 'Visayas', ',', 'Dinagat', 'Islands', ',', 'Surigao', 'del', 'Norte', ',', 'Surigao', 'del', 'Sur', ',', 'ug', 'Agusan', 'del', 'Norte.', 'Samtang', ',', 'aduna'y', 'hinay', 'ngadto', 'sa', 'kasarangan', 'ug', 'usahay', 'kusog', 'nga', 'pag-ulan', 'ang', 'masinati', 'sa', 'Agusan', 'del', 'Sur', ',', 'Davao', 'Region', ',', 'Northern', 'Mindanao', ',', 'ug', 'ubang', 'bahin', 'sa', 'Bicol', 'Region', 'ug', 'Visayas.', 'Tungod', 'sa', 'maong', 'kondisyon', ',', 'posible', 'nga', 'aduna'y', 'pagbaha', 'ug', 'pagdahili', 'sa', 'yuta', 'tungod', 'sa', 'ulan', ',', 'ilabi', 'na', 'sa', 'mga', 'lugar', 'nga', 'delikado', 'sa', 'maong', 'mga', 'panghitabo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 6, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 6, 0, 5, 6, 0, 5, 6, 6, 0, 5, 6, 6, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 5, 6, 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]
|
cebuaner
|
4,342
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['USA', 'KA', 'LANGYAW', ',', 'GIINGONG', 'GIATAKE', 'SA', 'LAING', '3', 'KA', 'MGA', 'LANGYAW', 'Usa', 'ka', 'langyaw', 'ang', 'giingong', 'giatake', 'sa', 'laing', 'tulo', 'ka', 'mga', 'langyaw', 'mga', 'alas-10', 'sa', 'gabii', 'niadtong', 'Biyernes', ',', 'Enero', '6', ',', '2023.', 'Giila', 'ang', 'biktima', 'nga', 'si', 'James', 'Daniel', 'Mostyn', ',', '58', 'anyos', ',', 'minyo', ',', 'ug', 'lumolupyo', 'sa', 'Sitio', 'Hawa', 'sa', 'Pob.', 'Dist.', '2', 'sa', 'Dauin.', 'Samtang', 'ang', 'mga', 'suspek', 'mao', 'sila', 'si', 'Mark', 'Higgins', ',', 'usa', 'ka', 'British', 'national', ',', 'Johnston', 'Shannon', 'Christopher', ',', '45', 'anyos', ',', 'usa', 'ka', 'American', 'national', ',', 'ug', 'Jerry', 'Lukanniuk', ',', '64', 'anyos.', 'Bag-ohay', 'lamang', ',', 'usa', 'ka', 'Day', 'Stuart', 'Ian', ',', '46', 'anyos', ',', 'ang', 'gipagawas', 'nga', 'wala'y', 'kaso.', 'Atol', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'wala'y', 'mga', 'passport', 'ang', 'upat', 'ka', 'mga', 'langyaw.', 'Sumala', 'pa', 'sa', 'report', ',', 'nisulod', 'ang', 'upat', 'ka', 'mga', 'langyaw', 'sa', 'lugar', 'sa', 'biktima', 'pinaagi', 'sa', 'pagsaka', 'sa', 'gate', 'diin', 'nagsinggit', 'sila', 'ug', 'nagpangita', 'sa', 'biktima.', 'Matod', 'pa', 'sa', 'saksi', ',', 'giatake', 'ni', 'Higgins', 'ang', 'biktima', 'sa', 'main', 'door', 'nga', 'niresulta', 'sa', 'pagkasamad', 'niini', 'sa', 'ulo.', 'Giingong', 'nagpabuto', 'og', 'pusil', 'si', 'Lukanniuk', 'samtang', 'nakigbugno', 'ang', 'biktima', 'nila', 'ni', 'Higgins', 'ug', 'Christopher', 'nga', 'niresulta', 'sab', 'sa', 'pagkasamad', 'sa', 'ilang', 'mga', 'nawong.', 'Narekober', 'sa', 'crime', 'scene', 'ang', 'duha', 'ka', 'brass', 'knucles', 'ug', 'giingong', 'gipanag-iya', 'ni', 'Christopher.', 'Nakuha', 'sab', 'ang', 'usa', 'ka', 'caliber', '45', 'pistol', 'nga', 'aduna'y', 'buhi', 'nga', 'bala', 'ug', 'giingong', 'gipanag-iya', 'ni', 'Lukanniuk.', 'Gidala', 'sa', 'NOPH', 'aron', 'matambalan', 'sila', 'si', 'Higgins', 'ug', 'Christophers', ',', 'samtang', 'anaa', 'na', 'sa', 'kustodiya', 'sa', 'Dauin', 'Police', 'Station', 'si', 'Lukanniuk.', 'Padayon', 'pang', 'giimbestigahan', 'sa', 'kapulisan', 'ang', 'motibo', 'sa', 'maong', 'panghitabo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 7, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 5, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,343
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Patay', 'ang', 'usa', 'ka', 'babaye', 'sa', 'lungsod', 'sa', 'Amlan', 'human', 'siya', 'gitigbas', 'og', 'makadaghan', 'samtang', 'naglakaw', 'sa', 'dalan', 'kagabii', ',', 'Enero', '9', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Suet', 'Rubio', 'Barete', ',', '25', 'anyos', ',', 'minyo', ',', 'nagpuyo', 'sa', 'Sitio', 'Kang-atid', 'sa', 'Barangay', 'Silab', 'sa', 'naasoy', 'nga', 'lungsod.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'Amlan', 'PNP', ',', 'pauli', 'na', 'unta', 'si', 'Barete', 'human', 'siya', 'nangumpra', 'sa', 'dihang', 'gibanhigan', 'kini', 'sa', 'wala', 'pa', 'mailhing', 'mamumuno.', 'Gitigbas', 'sa', 'suspek', 'si', 'Barete', 'og', 'makadaghan', 'sa', 'lain-laing', 'bahin', 'sa', 'lawas', 'niini.', 'Dead', 'on', 'the', 'spot', 'ang', 'biktima.', 'Padayon', 'pang', 'giimbestigaran', 'sa', 'kapulisan', 'sa', 'Amlan', 'ang', 'maong', 'linuog', 'nga', 'krimen', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,344
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sa', 'pagsulod', 'sa', 'bag-ong', 'tuig', ',', 'aduna', 'sab', 'mga', 'bag-ong', 'pangalan', 'sa', 'mga', 'bagyo', 'nga', 'gikabalak-ang', 'mosulod', 'sa', 'nasud', 'karong', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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
|
4,345
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TROUGH', 'SA', 'LPA', 'MAGDALA', 'OG', 'PAG-ULAN', 'SA', 'VISAYAS', ',', 'MINDANAO', 'Gilaoman', 'nga', 'magdala', 'og', 'pag-ulan', 'sa', 'pipila', 'ka', 'bahin', 'sa', 'Visayas', 'ug', 'Mindanao', 'ang', 'trough', 'sa', 'low', 'pressure', 'area', '(', 'LPA', ')', 'karong', 'Lunes', ',', 'Enero', '9', ',', '2022.', 'Sumala', 'pa', 'sa', 'PAGASA', ',', 'posibleng', 'masinati', 'sa', 'Visayas', ',', 'Mindanao', ',', 'Bicol', 'Region', ',', 'ug', 'sa', 'habagatang', 'bahin', 'sa', 'Palawan', 'apil', 'na', 'ang', 'Kalayaan', 'Islands', 'ang', 'madag-umon', 'nga', 'kalangitan', 'uban', 'sa', 'katag-katag', 'nga', 'pag-ulan', 'ug', 'pagdalugdog', 'tungod', 'sa', 'LPA.', 'Mahimong', 'aduna'y', 'pagbaha', 'o', 'pagdahili', 'sa', 'yuta', 'tungod', 'sa', 'kasarangan', 'ngadto', 'sa', 'kusog', 'nga', 'pag-ulan', 'sa', 'naasoy', 'nga', 'mga', '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, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,346
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sunog', 'niulbo', 'sa', 'usa', 'ka', 'residential', 'area', 'dapit', 'sa', 'Jose', 'Pro', 'Teves', '(', 'Cervantes', ')', 'Street', ',', 'Barangay', '8', ',', 'Dumaguete', 'City', 'karong', 'gabii', ',', 'Enero', '7', ',', '2023.', 'Matud', 'pa', 'sa', 'kabumberohan', ',', 'duha', 'ka', 'balay', 'ang', 'naigo', 'sa', 'maong', 'sunog', 'ug', 'napalong', 'na', 'kini', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,347
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', 'NANGITA', 'KA'G', 'TRABAHO', '?', 'Mao', 'kini', 'ang', 'mga', 'in-demand', 'nga', 'trabaho', 'nga', 'pwede', 'nimong', 'sudlan', 'sa', 'Pilipinas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 0]
|
cebuaner
|
4,348
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2023', 'MAAYONG', 'TUIG', 'ALANG', 'SA', 'GUGMA', 'UG', 'PAGPANGANAK', ',', 'MATUD', 'SA', 'USA', 'KA', 'FENG', 'SHUI', 'EXPERT', 'Gibutyag', 'ni', 'Feng', 'Shui', 'master', 'Marites', 'Allen', 'nga', 'mas', 'swerte', 'ang', '2023', 'kung', 'itandi', 'sa', 'niaging', 'tuig', ',', 'ilabi', 'na', 'kung', 'kabahin', 'sa', 'kasingkasing.', 'Sumala', 'pa', 'ni', 'Allen', ',', 'Year', 'of', 'the', 'Water', 'Rabbit', 'ug', '"', 'Center', 'Star', 'of', 'the', 'Year', '"', 'ang', '2023', 'nga', 'nagpasabot', 'nga', 'maayo', 'kining', 'tuig', 'alang', 'sa', 'gugma.', 'Gisubli', 'ni', 'Allen', 'nga', 'tungod', 'Year', 'of', 'the', 'Water', 'Rabbit', 'ang', '2023', ',', 'maayo', 'sab', 'kini', 'nga', 'tuig', 'aron', 'sa', 'pagpanganak.', 'Samtang', ',', 'nipahimangno', 'sab', 'si', 'Allen', 'sa', 'mga', 'gipanganak', 'sa', 'Year', 'of', 'the', 'Rat', 'ug', 'Rooster', 'nga', 'mahimong', 'magmatngon', 'bahin', 'sa', 'mga', 'sex', 'scandals', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 8, 0, 0, 0, 7, 8, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 7, 8, 8, 8, 8, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 1, 0, 0, 0, 0, 7, 8, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,349
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GRADUATE', 'SA', 'COSCA', ',', 'TOP', '5', 'SA', 'DECEMBER', '2022', 'RADTECH', 'BOARD', 'EXAM', 'Nalakip', 'si', 'Jose', 'Nico', 'Ramaila', 'Maicom', 'sa', 'mga', 'topnotchers', 'sa', 'December', '2022', 'Radiologic', 'Technologist', 'Licensure', 'Examination', '(', 'RTLE', ')', '.', 'Si', 'Maicom', ',', 'kinsa', 'nigradwar', 'sa', 'Colegio', 'de', 'Sta.', 'Catalina', 'de', 'Alejandria', ',', 'nakakuha', 'og', '89', '%', 'passing', 'rate', 'sa', 'RTLE.', 'Gipagawas', 'sa', 'Professional', 'Regulation', 'Commission', '(', 'PRC', ')', 'ang', 'resulta', 'sa', 'maong', 'exam', 'niadtong', 'Enero', '5', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 7, 8, 8, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 1, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,350
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INDIAN', 'RESTAURANT', 'SA', 'DGTE', ',', 'NAGSIRA', 'OG', '3', 'KA', 'ADLAW', 'TUNGOD', 'SA', 'KRISIS', 'SA', 'SIBUYAS', 'Tulo', 'ka', 'adlaw', 'nga', 'nagsira', 'ang', 'Roti', 'Boss', 'Curry', 'House', 'Restaurant', 'sa', 'Dumaguete', 'tungod', 'sa', 'kakulang', 'sa', 'sibuyas', 'sa', 'dakbayan.', 'Sirado', 'ang', 'maong', 'Indian', 'Restaurant', 'niadtong', 'Enero', '4-6', 'ug', 'mobalik', 'sila', 'pag-abri', 'karong', 'Sabado', ',', 'Enero', '7', ',', '2023.', 'Niadtong', 'Disyembre', ',', 'nikabat', 'na', 'og', 'kapin', '₱450', 'ang', 'presyo', 'kada', 'kilo', 'sa', 'sibuyas', 'sa', 'merkado', 'ning', 'dakbayan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,351
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INFLATION', 'RATE', 'NISAKA', 'NGADTO', 'SA', '8.1', '%', 'NIADTONG', 'DISYEMBRE', '2022', 'Mas', 'paspas', 'nga', 'nisaka', 'ang', 'inflation', 'rate', 'sa', 'nasud', 'niadtong', 'Disyembre', 'sa', 'niaging', 'tuig', 'tungod', 'sa', 'mas', 'taas', 'nga', 'presyo', 'sa', 'pinili', 'nga', 'mga', 'pagkaon', 'sama', 'sa', 'sibuyas', 'ug', 'uban', 'pang', 'mga', 'utan.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'niadtong', 'Huwebes', ',', 'Enero', '5', ',', '2023.', 'Nisaka', 'ang', 'consumer', 'price', 'index', 'ngadto', 'sa', '8.1', '%', ',', 'mas', 'paspas', 'sa', '8', '%', 'sa', 'niaging', 'bulan', 'ug', 'pinakataas', 'sukad', 'niadtong', 'Nobyembre', '2008.', 'Anaa', 'sa', '10.2', '%', 'ang', 'inflation', 'sa', 'pagkaon', 'ug', 'non-alcoholic', 'beverages', 'niadtong', 'Disyembre', 'gikan', 'sa', '10', '%', 'niadtong', 'Nobyembre', ',', 'samtang', 'ang', 'kinatibuk-ang', 'food', 'inflation', 'sa', 'national', 'level', 'nisaka', 'ngadto', 'sa', '10.6', '%', 'gikan', 'sa', '10.3', '%', 'sa', 'niaging', 'bulan.', 'Aduna'y', '"', 'substantial', '"', 'nga', 'kontribusyon', 'ang', 'sibuyas', 'sa', 'inflation', 'sa', 'niaging', 'bulan', 'human', 'ang', 'presyo', 'niini', 'nisaka', 'ngadtonsa', 'P700', 'kada', 'kilo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,352
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P1', 'NGA', 'PLITE', 'SA', 'LOCAL', ',', 'INT'L', 'FLIGHTS', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'seat', 'sale', 'promos', 'gikan', 'Manila', 'paingon', 'sa', 'lokal', 'nga', 'mga', 'destinasyon', 'ug', 'vice', 'versa', 'nga', 'ingon', 'kaubos', 'sa', 'P1.', 'Magamit', 'ang', 'promo', 'hangtod', 'sa', 'Enero', '6', ',', 'diin', 'pipila', 'sa', 'mga', 'rota', 'niini', 'mao', 'ang', 'Bohol', ',', 'Puerto', 'Princesa', ',', 'Coron', ',', 'Zamboanga', ',', 'ug', 'Boracay', ',', 'ug', 'uban', 'pa.', 'Aduna', 'sab', 'internasyonal', 'nga', 'mga', 'destinasyon', 'sama', 'sa', 'Tokyo', ',', 'Hong', 'Kong', ',', 'Dubai', ',', 'Bali', ',', 'Bangkok', ',', 'ug', 'uban', 'pa', 'sa', 'susamang', 'presyo.', 'Ang', 'maong', 'promo', 'aduna'y', 'travel', 'period', 'nga', 'gikan', 'sa', 'Pebrero', '1', 'hangtod', 'Mayo', '31', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 5, 0, 5, 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
|
4,353
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'NGA', 'NAG-PROPOSE', 'SA', 'IYANG', 'GF', ',', 'NIHANGYO', 'NGA', 'BAYARAN', 'ANG', 'KATUNGA', 'SA', 'ENGAGEMENT', 'RING', 'Usa', 'ka', 'lalaki', 'sa', 'Taiwan', 'ang', 'nakakuha', 'og', 'atensyon', 'gikan', 'sa', 'online', 'community', 'human', 'iyang', 'gihangyo', 'ang', 'iyang', 'fiance', 'nga', 'bayaran', 'ang', 'katunga', 'sa', 'iyang', 'gihatag', 'nga', 'engagement', 'ring.', 'Gi-share', 'sa', 'babayi', 'sa', 'social', 'media', 'ang', 'pag-propose', 'sa', 'iyang', 'uyab', 'diin', 'gihatagan', 'siya', 'og', 'diamond', 'ring', 'niini.', 'Upat', 'na', 'katuig', 'ang', 'mag-uyab', 'ang', 'duha', 'ug', 'giklaro', 'niya', 'nga', 'iyang', 'girespeto', 'ang', 'iyang', 'uyab', 'sa', 'pamatasan', 'niini', 'bahin', 'sa', 'kwarta.', 'Sa', 'dihang', 'niuli', 'na', 'sila', 'gikan', 'sa', 'marriage', 'proposal', ',', 'gipadayag', 'sa', 'lalaki', 'ang', 'tinuod', 'nga', 'kantidad', 'sa', 'singsing', 'nga', 'anaa', 'sa', 'P274,000', 'ug', 'gihangyo', 'ang', 'babayi', 'nga', 'hatagan', 'siya', 'sa', 'katunga', 'niini.', 'Gisubli', 'sa', 'lalaki', 'nga', 'alang', 'sa', 'duha', 'ka', 'tawo', 'ang', 'kaminyuon', ',', 'ug', 'tanan', 'nga', 'naa', 'nila', 'kinahanglang', 'parehas', 'nga', 'bahinon.', 'Wala', 'kini', 'nakamaayo', 'sa', 'ilang', 'relasyon', 'diin', 'wala', 'na', 'nipadayag', 'ang', 'lalaki', 'sa', 'iyang', '"', 'cost-sharing', '"', 'plan', ',', 'samtang', 'ang', 'babayi', 'wala', 'na', 'nagsul-ob', 'sa', 'engagement', 'ring.', 'Pipila', 'sab', 'ka', 'netizens', 'sa', 'online', 'ang', 'nagtambag', 'sa', 'babayi', 'nga', 'biyaan', 'ang', 'lalaki', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,354
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-share', 'sa', 'NASA', 'ang', 'usa', 'ka', 'hulagway', 'sa', 'Adlaw', ',', 'samtang', 'ni-transition', 'kita', 'ngadto', 'sa', '2023', 'nga', 'nagtimaan', 'sa', 'bag-ong', 'orbit', 'sa', 'Earth', 'sa', 'maong', 'star', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 5, 0, 0, 0, 0]
|
cebuaner
|
4,355
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'BPI', 'users', 'ang', 'nagreklamo', 'nga', 'nakuhaan', 'kuno', 'ang', 'ilang', 'available', 'balance', 'pinaagi', 'sa', '"', '0431', 'Debit', 'Memo', '"', 'nga', 'nigawas', 'sa', 'ilang', 'account', 'karong', 'buntag', ',', 'Jan.', '4', ',', '2023.', 'Sa', 'kadaghan', 'sa', 'nagreklamo', ',', 'nag-trending', 'sa', 'Twitter', 'sa', 'Pilipinas', 'ang', '"', '0431', 'Debit', 'Memo.', '"', 'Sa', 'opisyal', 'nga', 'pamahayag', 'sa', 'BPI', 'sa', 'Facebook', 'page', 'niini', ',', 'nipasalig', 'ang', 'bangko', 'nga', 'gi-trabaho', 'na', 'nila', 'ang', 'pag-ayo', 'sa', 'maong', 'problem'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,356
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'pawikan', 'ang', 'napalgang', 'patay', 'sa', 'kabaybayunan', 'sa', 'Barangay', 'Tapon', 'Norte', 'B', 'sa', 'lungsod', 'sa', 'San', 'Jose', 'karong', 'hapon', '(', 'Jan.', '3', ',', '2023', ')', '.', 'Sumala', 'pa', 'sa', 'taho', 'sa', 'San', 'Jose', 'PNP', ',', 'tulo', 'ka', 'adlaw', 'nang', 'patay', 'ang', 'maong', 'lalaking', 'pawikan', 'sa', 'dihang', 'napalgan', 'kini', 'sa', 'mga', 'residente', 'didto.', 'Duna', 'sad', 'kini', 'gibug-aton', 'nga', '80', 'kilogramos.', 'Daling', 'niresponde', 'ang', 'kapulisan', 'ug', 'mga', 'sakop', 'sa', 'Bantay', 'Dagat', 'sa', 'maong', 'insidente.', 'Human', 'niini', ',', 'gilubong', 'ra', 'sab', 'ang', 'patayng', 'lawas', 'sa', 'maong', 'pawikan', 'mga', '15', 'metros', 'ang', 'gilay-on', 'gikan', 'sa', 'baybay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 6, 6, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,357
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TRICYCLE', 'DRIVER', 'GIULI', 'ANG', 'NAKIT-ANG', 'WALLET', 'NGA', 'ADUNA'Y', 'KWARTA', ',', 'IMPORTANTENG', 'MGA', 'DOKUMENTO', 'Usa', 'ka', 'tricycle', 'driver', 'ang', 'nitahan', 'sa', 'Bayawan', 'City', 'Police', 'Station', 'aron', 'ihatod', 'ang', 'iyang', 'nakit-ang', 'wallet', 'nga', 'aduna'y', 'sulod', 'nga', 'mga', 'importanteng', 'dokumento', 'ug', 'kwarta', 'nga', 'mobalor', 'og', 'P13,000.', 'Gihatod', 'ni', 'Epifanio', 'Dabis', 'ang', 'pitaka', 'sa', 'Police', 'Station', 'aron', 'mauli', 'kini', 'sa', 'tag-iya.', 'Lumolupyo', 'sa', 'GK', 'Village', 'sa', 'Barangay', 'Villareal', 'sa', 'Bayawan', 'City', 'si', 'Dabis.', 'Nakuha', 'na', 'sa', 'tag-iya', 'ang', 'wallet', 'nga', 'si', 'Rolando', 'Macapobre', 'Jr.', 'kinsa', 'residente', 'sa', 'Purok', 'Gemelina', 'sa', 'Barangay', 'Villareal', 'sa', 'naasoy', 'nga', 'dakbayan.', 'Dako', 'sab', 'ang', 'pagpasalamat', 'ni', 'Macapobre', 'ngadto', 'ni', 'Dabis', 'sa', 'pag-uli', 'niini', 'sa', 'iyang', 'pitaka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 6, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,358
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Unang', 'adlaw', 'sa', '2023', 'apan', 'halos', 'walay', 'eroplano', 'nga', 'makitang', 'galupad', 'karon', 'bisan', 'asa', 'sa', 'Pilipinas', 'human', 'nga', 'gi-“hold”', 'ang', 'mga', 'flight', 'sa', 'Ninoy', 'Aquino', 'International', 'Airport', '(', 'NAIA', ')', 'tungod', 'sa', '“technical', 'issues.”'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0]
|
cebuaner
|
4,359
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitaliwan', 'na', 'sa', 'laing', 'kalibutan', 'ang', 'kanhing', 'Santo', 'Papa', 'nga', 'si', 'Pope', 'Benedict', 'XVI', 'karong', 'adlawa', ',', 'Dec.', '31', ',', '2022', ',', 'sa', 'edad', 'nga', '95', 'anyos.', 'Kini', 'gikumpirmar', 'sa', 'Vatican', 'City', 'pinaagi', 'sa', 'usa', 'ka', 'pamahayag.', 'Una', 'nang', 'gibutyag', 'ni', 'Pope', 'Francis', 'nga', 'nagkagrabe', 'ang', 'kahimtang', 'sa', 'panglawas', 'ni', 'Pope', 'Benedict', 'XVI', 'pipila', 'ka', 'adlaw', 'lang', 'ang', 'nilabay.', 'Gani', ',', 'nihangyo', 'pa', 'si', 'Pope', 'Francis', 'ngadto', 'sa', 'mga', 'Katoliko', 'nga', 'mag-ampo', 'alang', 'sa', 'kaayuhan', 'sa', 'kanhing', 'Santo', 'Papa.', 'Nahimong', 'Santo', 'Papa', 'si', 'Pope', 'Benedict', 'XVI', 'niadtong', 'tuig', '2005', 'human', 'namatay', 'si', 'Pope', 'John', 'Paul', 'II.', 'Ni-resign', 'siya', 'sa', 'pagka-Santo', 'Papa', 'niadtong', '2013', 'tungod', 'sa', 'mga', 'problema', 'sa', 'kahimsog', ',', 'ug', 'siya', 'gipulihan', 'karon', 'sa', 'kasamtangang', 'Papa', 'nga', 'si', 'Pope', 'Francis', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 7, 8, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
|
cebuaner
|
4,360
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GALLUP', 'YEAREND', 'SURVEY', ':', 'PH', 'PINAKAMALIPAYONG', 'NASUD', 'SA', 'TIBUOK', 'KALIBUTAN', 'SA', '2022', 'Ang', 'Pilipinas', 'mao', 'ang', 'pinakamalipayon', 'nga', 'nasud', 'sa', 'tibuok', 'kalibutan', 'sa', '2022', ',', 'sumala', 'pa', 'sa', 'pinakabag-ong', '2022', 'End', 'of', 'Year', 'Survey', 'sa', 'Gallup', 'International', 'Association', '(', 'GIA', ')', '.', 'Sa', '34', 'ka', 'nasud', 'nga', 'sakop', 'sa', 'maong', 'survey', ',', 'anaa', 'sa', 'top', 'spot', 'ang', 'Pilipinas', 'diin', 'aduna', 'kini', 'net', 'score', 'nga', '75', '%', '.', 'Anaa', 'sab', 'ang', 'Mexico', ',', 'Malaysia', ',', 'Afghanistan', ',', 'ug', 'Ecuador', 'nga', 'nikompleto', 'sa', 'top', '5.', 'Samtang', '39', '%', 'sa', 'mga', 'Pilipino', 'ang', 'malaumon', 'nga', 'mahimong', 'mas', 'maayo', 'ang', '2023', 'kesa', 'sa', '2022', ',', '52', '%', 'ang', 'nag-ingon', 'nga', 'kini', 'pareha', 'lang.', 'Anaa', 'sab', '5', '%', 'ang', 'nag-ingon', 'nga', 'mas', 'grabe', 'pa', 'ang', '2023', 'ug', '4', '%', 'ang', 'wala', 'pa', 'kahibalo.', 'Ang', 'Nigeria', 'ug', 'Pakistan', 'mao', 'ang', 'top', '2', 'nga', 'pinakamalaumon', 'nga', 'nasud', 'sa', 'kalibutan', ',', 'uban', 'sa', 'Kazakhstan', '(', '#', '3', ')', ',', 'Pilipinas', '(', '#', '4', ')', ',', 'ug', 'India', '(', '#', '5', ')', 'nga', 'nikompleto', 'sa', 'top', '5', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 5, 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, 3, 4, 4, 4, 4, 4, 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, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,361
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CONTRACTUAL', ',', 'JOB', 'ORDER', 'WORKERS', 'SA', 'DGTE', 'MAKADAWAT', 'OG', 'P3,000', 'ISIP', 'GRATUITY', 'PAY', 'Makadawat', 'og', 'P3,000', 'matag', 'usa', 'ang', 'mga', 'kwalipikadong', 'Contract', 'of', 'Service', 'ug', 'Job', 'Order', 'workers', 'sa', 'kagamhanang', 'syudad', 'sa', 'Dumaguete.', 'Giaprobahan', 'kini', 'ni', 'Mayor', 'Felipe', 'Remollo', 'subay', 'sa', 'Administrative', 'Order', 'No.', '03', 'sa', 'Office', 'of', 'the', 'President', 'ug', 'ubos', 'sa', 'pagkaanaa', 'sa', 'pondo', 'sa', 'Local', 'Government', 'Unit.', 'Ang', 'paghatag', 'sa', 'maong', 'gratuity', 'pay', 'anaa', 'sa', 'kinatibuk-ang', 'P6', 'milyon', 'alang', 'sa', 'kapin', '1,500', 'ka', 'mga', 'kwalipikadong', 'CO', 'ug', 'JO.', 'Gisubli', 'sab', 'ni', 'Mayor', 'Remollo', 'nga', 'pagpakita', 'kini', 'og', 'apresasyon', 'sa', 'kakugi', 'ug', 'pagkamaunongon', 'sa', 'pag-alagad', 'sa', 'maong', 'mga', 'trabahante.', 'Ang', 'gratuity', 'pay', 'nga', 'P3,000', ',', 'dugang', 'kini', 'sa', '25', 'kilos', 'nga', 'rice', 'subsidy', 'nga', 'ihatag', 'human', 'makompleto', 'ang', 'proseso', 'ug', 'laing', '5', 'kilos', 'nga', 'bugas', 'nga', 'nahatag', 'na', 'isip', 'personal', 'gift', 'sa', 'mayor', 'sa', 'matag', 'CO', 'ug', 'JO', 'worker', 'sa', 'LGU-dumaguete', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 8, 8, 0, 3, 4, 4, 4, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,362
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SOLON', ':', 'DILI', 'ANGAY', 'MABALAKA', 'ANG', 'PUBLIKO', 'KUNG', 'DILI', 'DAYON', 'MAKAREHISTRO', 'SA', 'SIM', 'CARDS', 'Usa', 'ka', 'magbabalaod', 'ang', 'nipasalig', 'sa', 'publiko', 'nga', 'wala'y', 'angay', 'kabalak-an', 'kung', 'dili', 'dayon', 'makarehistro', 'sa', 'ilang', 'subscriber', 'identity', 'module', '(', 'SIM', ')', '.', 'Gisubli', 'ni', 'Kabayan', 'Rep.', 'Ron', 'Salo', 'nga', 'aduna'y', '180', 'ka', 'adlaw', 'alang', 'sa', 'maong', 'aktibidad.', 'Sumala', 'pa', 'ni', 'Salo', ',', 'kung', 'makita', 'sa', 'gobyerno', 'nga', 'kinahanglang', 'lugwayan', 'ang', '180-day', 'period', 'nga', 'gipahayag', 'sa', 'Republic', 'Act', 'No.', '11934', 'o', 'SIM', 'Registration', 'Act', ',', 'mahimo', 'kini', 'nilang', 'i-extend.', 'Gipahayag', 'kini', 'ni', 'Salo', 'human', 'pipila', 'ka', 'mga', 'tag-iya', 'sa', 'mobile', 'phone', 'ang', 'naglisod', 'sa', 'pagrehistro', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'SIM', 'cards', ',', 'diin', 'daghan', 'sab', 'ang', 'wala', 'nakaabot', 'sa', 'registration', 'site', 'nga', 'gihatag', 'sa', 'gobyerno', 'ug', 'telecommunication', 'companies', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,363
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAGTIAYON', 'NGA', 'ADUNAY', 'PAREHA', 'NGA', 'BIRTHDAY', ',', 'GI-WELCOME', 'ANG', 'ILANG', 'BABY', 'SA', 'ILANG', 'ADLAW', 'SAB', 'NGA', 'NATAWHAN', 'Usa', 'ka', 'magtiayon', 'nga', 'aduna'y', 'pareha', 'nga', 'birthday', 'ang', 'ni-welcome', 'sa', 'ilang', 'anak', 'sa', 'ilang', 'adlaw', 'sab', 'nga', 'natawhan.', 'Mao', 'kini', 'ang', 'gi-share', 'sa', 'Huntsville', 'Hospital', 'for', 'Women', 'and', 'Children', 'sa', 'Alabama', 'sa', 'social', 'media', 'niadtong', 'Disyembre', '20', ',', '2022.', 'Sumala', 'pa', 'sa', 'maong', 'medical', 'institution', ',', 'ang', 'pagpanganak', 'usa', 'ka', '"', 'exciting', 'time', 'for', 'any', 'family.', '"', 'Apan', 'sa', 'kaso', 'nila', 'ni', 'Cassidy', 'ug', 'Dylan', 'Scott', ',', 'usa', 'kini', 'ka', '"', 'extra', 'special', 'because', 'they', 'all', 'share', 'the', 'same', 'birthday.', '"', 'Dugang', 'pa', 'sa', 'maong', 'institusyon', ',', 'ang', 'maong', 'kaso', '"', 'one', 'in', 'a', '133,000', 'chance', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 3, 4, 4, 4, 4, 4, 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, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,364
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'swerte', 'nga', 'nipatad', 'ang', 'naka-jackpot', 'sa', 'kapin', 'P114', 'milyon', 'human', 'sa', 'pag-draw', 'sa', 'winning', 'combination', 'sa', 'Mega', 'Lotto', '6', '/', '45', 'niadtong', 'Biyernes', ',', 'Disyembre', '23', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,365
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LALAKI', 'NI-AMBAK', 'GIKAN', 'SA', 'GISAKYANG', 'BARKO', 'Usa', 'ka', 'lalaki', 'ang', 'ni-ambak', 'gikan', 'sa', 'gisakyang', 'barko', 'nga', 'paingon', 'unta', 'sa', 'Cagayan', 'de', 'Oro', 'City', 'sa', 'kadlawon', 'niadtong', 'Sabado', ',', 'Disyembre', '24', ',', '2022.', 'Mao', 'kini', 'ang', 'gikompirma', 'sa', 'Philippine', 'Coast', 'Guard', 'sa', 'Central', 'Visayas.', 'Nahitabo', 'ang', 'insidente', 'samtang', 'ang', 'barko', 'nga', 'gikan', 'sa', 'pantalan', 'sa', 'Cebu', 'City', 'nagbiyahe', 'sa', 'kadagatan', 'sa', 'probinsya', 'sa', 'Siquijor.', 'Sumala', 'pa', 'ni', 'Ruby', 'Jabaybay', ',', 'usa', 'sa', 'mga', 'pasahero', 'sa', 'Lite', 'Ferry', '19', ',', 'gianunsyo', 'sa', 'kapitan', 'nga', 'kinahanglan', 'nilang', 'pangitaon', 'ang', 'usa', 'ka', 'lalaking', 'pasahero', 'sa', 'dili', 'pa', 'sila', 'mopadayon', 'sa', 'destinasyon.', '"', 'Ang', 'barko', 'nituyok', 'tuyok', 'una', 'ug', 'pipila', 'ka', 'oras', 'sa', 'dapit', 'diin', 'niambak', 'ang', 'lalaki', 'aron', 'pangitaon', 'kini', 'apan', 'wa', 'na', 'gyud', 'makit-i', ',', '"', 'sumala', 'pa', 'ni', 'Jabaybay.', 'Luwas', 'nga', 'naabot', 'ang', 'barko', 'ug', 'uban', 'pang', 'mga', 'pasahero', 'sa', 'pantalan', 'sa', 'Cagayan', 'de', 'Oro', 'mga', 'alas-8', 'sa', 'buntag', 'sa', 'naasoy', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 7, 8, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,366
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BUGNAW', 'NGA', 'PANAHON', 'MASINATI', 'SA', 'PASKO', 'Magpadayon', 'ang', 'bugnaw', 'nga', 'hangin', 'dala', 'sa', 'kusog', 'nga', 'Northeast', 'Monsoon', 'o', 'Amihan', 'sa', 'dakong', 'bahin', 'sa', 'nasud', 'hangtod', 'sa', 'adlaw', 'sa', 'Pasko.', 'Gawas', 'sa', 'bugnaw', 'nga', 'panahon', ',', 'magdala', 'sab', 'kini', 'og', 'madag-umon', 'nga', 'kalangitan', 'ug', 'pag-ulan', 'nga', 'hiniusang', 'epekto', 'sa', 'Amihan', 'ug', 'Shear', 'Line', ',', 'diin', 'pinakaapektado', 'ang', 'silangang', 'bahin', 'sa', 'Luzon', 'ug', 'Visayas.', 'Kinahanglang', 'mag-andam', 'sa', 'posibleng', 'kalit', 'nga', 'pagbaha', 'ug', 'pagdahili', '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, 7, 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, 0, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,367
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGOR', 'SWIMMING', 'TEAM', 'NAKADAOG', 'OG', '8', 'GOLD', 'MEDAL', ',', '2', 'BRONZE', 'MEDAL', 'SA', 'BATANG', 'PINOY', '2022', 'Nakadaog', 'ang', 'swimming', 'team', 'sa', 'Negros', 'Oriental', 'og', 'walo', 'ka', 'gold', 'medal', 'ug', 'duha', 'ka', 'bronze', 'medal', 'sa', 'Batang', 'Pinoy', '2022', 'nga', 'gipahigayon', 'sa', 'Vigan', ',', 'Ilocos', 'Sur', 'niadtong', 'Disyembre', '17-21', ',', '2022.', 'Nakakuha', 'si', 'Kacie', 'Gabrielle', 'Tionko', 'og', 'lima', 'ka', 'gold', 'medal', 'sa', '100m', ',', '200m', ',', '400m', ',', '50m', 'freestyle', 'ug', '100m', 'breaststroke.', 'Samtang', 'si', 'Karl', 'Fernandez', ',', 'nakadaog', 'sab', 'og', 'tulo', 'ka', 'gold', 'medal', 'ug', 'usa', 'ka', 'bronze', 'medal.', 'Naka-gold', 'medal', 'siya', 'sa', '50m', ',', '200m', 'ug', '100m', 'breaststroke', ',', 'ug', 'bronze', 'medal', 'sa', '200m', 'freestyle.', 'Nakadaog', 'sab', 'si', 'Marco', 'Sayson', 'og', 'bronze', 'medal', 'sa', '200m', 'backstroke.', 'Sa', 'maong', 'resulta', ',', 'anaa', 'sa', 'ika-22', 'nga', 'pwesto', 'sa', 'medal', 'tally', 'o', 'ranking', 'ang', 'Negros', 'Oriental', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0]
|
cebuaner
|
4,368
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['14', 'BALOT', 'VENDORS', ',', 'NAKADAWAT', 'OG', 'STAINLESS', 'STEEL', 'CARTS', 'ISIP', 'SUPORTA', 'SA', 'ILANG', 'PANGINABUHIAN', 'Giapod-apod', 'ang', '14', 'ka', 'stainless', 'steel', 'vending', 'carts', 'uban', 'sa', 'mga', 'accessories', 'ngadto', 'sa', 'mga', 'tindera', 'og', 'balot', 'sa', 'dakbayan', 'sa', 'Dumaguete.', 'Gidonar', 'kini', 'sa', 'Universal', 'Robina', 'Corporation', 'aron', 'mapalambo', 'ang', 'kalimpyo', 'ug', 'mapasayon', 'ang', 'pagsunod', 'sa', 'Food', 'Safety', 'Ordinance', 'sa', 'dakbayan.', 'Matag', 'nakadawat', ',', 'aduna'y', 'libre', 'nga', 'vending', 'cart', ',', 'usa', 'ka', 'lamesa', ',', 'upat', 'ka', 'bangko', ',', 'cooler', ',', 'duha', 'ka', 'outdoor', 'umbrella', 'ug', 'nagkalain-lain', 'merchandise', 'o', 'goods.', 'Gipangunahan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ang', 'paghatag', 'niini', 'ug', 'giawhag', 'ang', 'mga', 'benepisyaryo', 'nga', 'ampingan', 'ang', 'ilang', 'vending', 'carts', 'ug', 'pagpabiling', 'limpyo', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'pwesto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 5, 0, 0, 0, 3, 4, 4, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,369
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Walay', 'klase', 'ug', 'walay', 'trabaho', 'karong', 'Lunes', ',', 'Dec.', '26', ',', '2022', ',', 'subay', 'sa', 'proklamasyon', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'Sumala', 'pa', 'sa', 'proklamasyon', 'ni', 'Marcos', ',', 'gideklarar', 'niya', 'ang', 'maong', 'holiday', 'aron', 'mahatagan', 'og', 'igong', 'panahon', 'ang', 'publiko', 'nga', 'makasaulog', 'sa', 'Pasko', 'uban', 'ang', 'ilang', 'mga', 'minahal', 'sa', 'kinabuhi.', 'Gimando', 'na', 'sab', 'sa', 'Presidente', 'ang', 'Department', 'of', 'Labor', 'and', 'Employment', '(', 'DOLE', ')', 'pagpanday', 'og', 'memorandum', 'aron', 'mapatuman', 'ang', 'iyang', 'proklamasyon', 'alang', 'sa', 'pribadong', 'sektor', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 2, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,370
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['3', 'PATAY', 'SA', 'GITUOHANG', 'ROBBERY', 'WITH', 'HOMICIDE', 'SA', 'ZAMBOANGUITA', 'Tulo', 'ang', 'patay', 'sa', 'gituohang', 'Robbery', 'with', 'Homicide', 'sa', 'Sitio', 'Cambilo', ',', 'Barangay', 'Najandig', ',', 'Zamboanguita', 'niadtong', 'Disyembre', '20', ',', '2022.', 'Giila', 'ang', 'mga', 'biktima', 'nga', 'sila', 'si', 'Pedro', 'Catalonia', ',', '70', 'anyos', ',', 'uban', 'sa', 'iyang', 'asawa', 'nga', 'si', 'Servana', 'Catalonia', ',', '65', 'anyos', ',', 'ug', 'ilang', 'anak', 'nga', 'si', 'Marites', 'Catalonia', ',', '43', 'anyos', ',', 'kinsa', 'puros', 'mag-uuma', 'ug', 'lumolupyo', 'sa', 'naasoy', 'nga', 'lugar.', 'Sumala', 'pa', 'sa', 'report', ',', 'niadto', 'sa', 'kapulisan', 'ang', 'anak', 'sa', 'mga', 'biktima', 'nga', 'Retchiel', 'Catalonia', 'uban', 'sa', 'barangay', 'kagawad', 'aron', 'ipahibalo', 'nga', 'nakit-ang', 'patay', 'ang', 'mga', 'biktima', 'sa', 'ilang', 'pinuy-anan.', 'Gisubli', 'ni', 'Retchiel', 'nga', 'gikawatan', 'ang', 'balay', 'sa', 'mga', 'biktima', 'tungod', 'nawala', 'ang', 'pipila', 'ka', 'mga', 'importante', 'nga', 'butang', 'lakip', 'na', 'ang', 'kwarta.', 'Dugang', 'pa', 'sa', 'report', ',', 'pisikal', 'nga', 'giatake', 'ang', 'mga', 'biktima', 'pinaagi', 'sa', 'paghapak', 'og', 'gahi', 'nga', 'butang', 'sa', 'ilang', 'kalawasan', 'diin', 'makita', 'ang', 'mga', 'bun-og', 'niini.', 'Nagkagubot', 'sab', 'ang', 'personal', 'nga', 'mga', 'butang', 'sa', 'mga', 'biktima', 'ug', 'giingong', 'pipila', 'ka', 'mga', 'butang', 'ang', 'nawala.', 'Matod', 'pa', 'sa', 'kapulisan', ',', 'posible', 'nga', 'pagpangawat', 'ang', 'motibo', 'sa', 'insidente.', 'Sa', 'pagkakaron', ',', 'padayon', 'pa', 'kining', 'gi-imbestigahan', 'sa', 'kapulisan', 'aron', 'masikop', 'ang', 'suspek', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,371
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BALAUDON', 'NGA', 'NAGTINGUHA', 'SA', 'FREE', 'TUITION', 'ALANG', 'SA', 'LAW', 'STUDENTS', ',', 'GIDUSO', 'Gipasaka', 'ni', 'Senator', 'Raffy', 'Tulfo', 'ang', 'usa', 'ka', 'balaudnon', 'nga', 'nagtinguha', 'sa', 'free', 'tuition', 'alang', 'sa', 'law', 'students', 'sa', 'state', 'universities', 'ug', 'colleges', '(', 'SUCs', ')', '.', 'Giduso', 'ni', 'Sen.', 'Tulfo', 'ang', 'Senate', 'Bill', 'No.', '1610', 'o', 'ang', 'Free', 'Legal', 'Education', 'Act', 'of', '2023.', 'Tumong', 'niini', 'nga', 'madungagan', 'ang', 'legal', 'profession', 'workforce', 'pinaagi', 'sa', 'mandatory', 'return', 'service', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 0, 7, 8, 8, 8, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,372
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LGU-DUMAGUETE', 'MOHATAG', 'OG', 'P3,000', 'NGA', 'HONORARIA', 'ALANG', 'SA', 'PUBLIC', 'SCHOOL', 'TEACHERS', 'Giaprobahan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ang', 'paghatag', 'og', 'P3,000', 'isip', 'honoraria', 'sa', 'matag', 'usa', 'sa', '913', 'ka', 'mga', 'magtutudlo', 'ilalom', 'sa', 'DepEd', 'Dumaguete', 'City', 'Schools', 'Division', 'base', 'sa', 'letter', 'request', 'ni', 'Superintendent', 'Dr.', 'Gregorio', 'Cyrus', 'Elejorde.', 'Giaprobahan', 'sab', 'sa', 'Sangguniang', 'Panlungsod', 'ang', 'paggahin', 'og', 'P2.7', 'milyon', 'aron', 'mapondohan', 'ang', 'paghatag', 'og', 'honoraria', 'alang', 'sa', 'public', 'school', 'teachers.', 'Sumala', 'pa', 'ni', 'Mayor', 'Remollo', ',', 'ang', 'paghatag', 'og', 'honorarium', 'usa', 'ka', 'pagpasalamat', 'sa', 'City', 'Government', 'sa', 'dedikasyon', 'sa', 'mga', 'magtutudlo', 'sa', 'pagserbisyo', 'sa', 'mga', 'estudyante', 'lakip', 'na', 'ang', 'pagsuporta', 'sa', 'mga', 'programa', ',', 'proyekto', 'ug', 'aktibidad', 'sa', 'city', 'administration', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,373
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['REGULAR', ',', 'CASUAL', 'EMPLOYEES', 'SA', 'CITY', 'GOV'T', 'MAKADAWAT', 'OG', 'P15,000', 'MATAG', 'USA', ';', 'JOB', 'ORDER', 'WORKERS', 'MAKADAWAT', 'SAB', 'OG', 'RICE', 'SUBSIDY', 'Hatagan', 'og', 'P15,000', 'nga', 'cash', 'incentive', 'ang', 'matag', 'usa', 'sa', '960', 'ka', 'regular', 'ug', 'casual', 'employees', ',', 'ug', '25', 'kilos', 'nga', 'rice', 'subsidy', 'matag', 'usa', 'alang', 'sa', '1,518', 'ka', 'Job', 'Order', 'workers', 'sa', 'City', 'Government', 'sa', 'Dumaguete.', 'Giaprobahan', 'kini', 'ni', 'Mayor', 'Felipe', 'Remollo', 'subay', 'sa', 'Administrative', 'Orders', 'No.', '1', 'ug', '2', 'nga', 'gi-isyu', 'ni', 'President', 'Ferdinand', 'Marcor', 'Jr.', 'niadtong', 'Disyembre', '16', ',', '2022.', 'Gipasa', 'sa', 'Sangguniang', 'Panlungsod', 'ang', 'approriation', 'ordinances', 'nga', 'nagtugot', 'sa', 'pagpagawas', 'og', 'dugang', 'nga', 'mga', 'benepisyo', 'alang', 'sa', 'tanan', 'local', 'government', 'employees', 'nga', 'nagkantidad', 'og', 'P15.4', 'milyon', 'sa', 'Service', 'Recognition', 'Incentive', 'ug', 'P4', 'milyon', 'sa', 'one-time', 'rice', 'subsidy', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 2, 0, 0, 7, 8, 8, 8, 0, 7, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,374
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['LGU-DUMAGUETE', 'MOHATAG', 'OG', 'P3,000', 'NGA', 'HONORARIA', 'ALANG', 'SA', 'PUBLIC', 'SCHOOL', 'TEACHERS', 'Giaprobahan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ang', 'paghatag', 'og', 'P3,000', 'isip', 'honoraria', 'sa', 'matag', 'usa', 'sa', '913', 'ka', 'mga', 'magtutudlo', 'ilalom', 'sa', 'DepEd', 'Dumaguete', 'City', 'Schools', 'Division', 'base', 'sa', 'letter', 'request', 'ni', 'Superintendent', 'Dr.', 'Gregorio', 'Cyrus', 'Elejorde.', 'Giaprobahan', 'sab', 'sa', 'Sangguniang', 'Panlungsod', 'ang', 'paggahin', 'og', 'P2.7', 'milyon', 'aron', 'mapondohan', 'ang', 'paghatag', 'og', 'honoraria', 'alang', 'sa', 'public', 'school', 'teachers.', 'Sumala', 'pa', 'ni', 'Mayor', 'Remollo', ',', 'ang', 'paghatag', 'og', 'honorarium', 'usa', 'ka', 'pagpasalamat', 'sa', 'City', 'Government', 'sa', 'dedikasyon', 'sa', 'mga', 'magtutudlo', 'sa', 'pagserbisyo', 'sa', 'mga', 'estudyante', 'lakip', 'na', 'ang', 'pagsuporta', 'sa', 'mga', 'programa', ',', 'proyekto', 'ug', 'aktibidad', 'sa', 'city', 'administration', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,375
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mao', 'kini', 'ang', 'NGC', '6956', ',', 'usa', 'ka', '"', 'spiral', 'galaxy', 'of', 'bright', 'blue', 'swirls.', '"', 'Nahimutang', 'kini', 'sa', 'gilay-on', 'nga', '214', 'million', 'light-years', 'sa', 'constellation', 'nga', 'Delphinus', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0]
|
cebuaner
|
4,376
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Subay', 'sa', 'bag-ong', 'balaod', 'nga', 'SIM', 'Registration', 'Act', '(', 'SRA', ')', ',', 'kinahanglan', 'nga', 'iparehistro', 'ang', 'gipanag-iyang', 'SIM', 'sugod', 'karong', 'Disyembre', '27', ',', '2022', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,377
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P88', 'NGA', 'PLITE', 'ALANG', 'SA', 'DOMESTIC', 'DESTINATIONS', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'one-way', 'base', 'fare', 'nga', 'ingon', 'kaubos', 'sa', 'P88', 'alang', 'sa', 'domestic', 'destinations.', 'Mahimong', 'mo-book', 'ang', 'mga', 'pasahero', 'sa', 'maong', 'promo', 'flight', 'hangtod', 'sa', 'Enero', '2', ',', '2023.', 'Aduna', 'kini', 'travel', 'period', 'gikan', 'Disyembre', '19', ',', '2022', 'hangtod', 'Mayo', '31', ',', '2023', 'Bag-ohay', 'lamang', ',', 'gianunsyo', 'sab', 'sa', 'maong', 'airline', 'ang', 'ilang', 'international', 'seat', 'sale', 'nga', 'ingon', 'kaubos', 'sa', 'P699', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,378
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ang', 'Negros', 'Oriental', 'State', 'University', '(', 'NORSU', ')', '-', 'Dumaguete', 'sa', 'mga', 'nanguna', 'nga', 'tunghaan', 'sa', 'nasud', 'nga', 'aduna'y', 'dako', 'nga', 'kinatibuk-ang', 'passing', 'rate', 'sa', 'Licensure', 'Examination', 'for', 'Teachers', '(', 'LET', ')', '.', 'Anaa', 'sa', 'top', '9', 'ang', 'NORSU', 'sa', 'elementary', 'level.', 'Nakakuha', 'kini', 'og', '90', '%', 'nga', 'passing', 'rate', 'diin', '108', 'ang', 'mga', 'nakapasar', 'sa', '120', 'ka', 'mga', 'nikuha', 'sa', 'maong', 'exam.', 'Gipagawas', 'sa', 'Professional', 'Regulation', 'Commission', '(', 'PRC', ')', 'ang', 'resulta', 'karong', 'adlawa', ',', 'Disyembre', '17', ',', '2022', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 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, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,379
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'migradwar', 'sa', 'Silliman', 'University', 'ang', 'nalakip', 'sa', 'Top', '9', 'sa', '2022', 'Licensure', 'Examination', 'for', 'Teachers', '(', 'LET', ')', '.', 'Nakakuha', 'si', 'Rine', 'Christelle', 'Galarpe', 'Anfone', 'og', '92.60', 'passing', 'rate', 'sa', 'secondary', 'level.', 'Gipagawas', 'sa', 'Professional', 'Regulation', 'Commission', '(', 'PRC', ')', 'ang', 'mga', 'resulta', 'sa', 'maong', 'board', 'exam', 'karong', 'adlawa', ',', 'Disyembre', '17', ',', '2022', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 0, 0, 7, 8, 8, 8, 8, 8, 8, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,380
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BAGYONG', 'ODETTE', 'NAGBILIN', 'OG', 'KADAOT', 'SA', 'NEGROS', 'ORIENTAL', 'Karong', 'adlawa', 'niadtong', '2021', ',', 'nagbilin', 'og', 'dakong', 'kadaot', 'ang', 'Bagyong', 'Odette', '(', 'international', 'name', ':', 'Rai', ')', 'sa', 'pipila', 'ka', 'parte', 'sa', 'nasud.', 'Tungod', 'sa', 'maong', 'Super', 'Typhoon', ',', 'pipila', 'ka', 'mga', 'indibidwal', 'ang', 'nakabsan', 'sa', 'ilang', 'kinabuhi.', 'Sa', 'Negros', 'Oriental', ',', '74', 'ang', 'namatay', ',', '99', 'ang', 'naangol', ',', 'ug', '22', 'ang', 'nawala.', '#', 'ThrowBack'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[7, 8, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,381
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NTC', 'NAGPASIDAAN', 'NA', 'SAB', 'SA', 'MGA', 'BAG-ONG', 'SCHEMES', 'SA', 'TEXT', 'SCAMS', 'Nagpasidaan', 'ang', 'National', 'Telecommunications', 'Commission', '(', 'NTC', ')', 'sa', 'pagdaghan', 'sa', 'mga', 'bag-ong', 'schemes', 'nga', 'nagpakaaron-ingnon', 'nga', 'gambling', 'sites', 'ug', 'sikat', 'nga', 'business', 'entities', 'nga', 'makapadani', 'sa', 'mga', 'subscriber', 'nga', 'moapil', 'sa', 'lain-laing', 'mga', 'dula', 'sa', 'kahigayunan', 'o', 'bonus', 'cash', 'offering', 'ug', 'ubang', 'susamang', 'money', 'scams', 'nga', 'gi-target', 'ang', 'publiko.', 'Pagsunod', 'kini', 'sa', 'memorandum', 'niadtong', 'Disyembre', '6', ',', '2022', 'nga', 'gi-isyu', 'sa', 'Commissioner', 'bahin', 'sa', '"', 'LATEST', 'TEXT', 'SCAM', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,382
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SIM', 'CARD', 'REGISTRATION', ',', 'IPATUMAN', 'KARONG', 'DISYEMBRE', '27', 'Nangandam', 'na', 'ang', 'mga', 'kompanya', 'sa', 'telecommunications', 'sa', 'pagsugod', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'SIM', 'registration', 'portals', 'sa', 'mga', 'mosunod', 'nga', 'semana', ',', 'samtang', 'ibaligya', 'ang', 'mga', 'bag-ong', 'SIM', 'cards', 'nga', 'anaa', 'sa', '"', 'deactivated', 'mode.', '"', 'Ipatuman', 'ang', 'mandatory', 'rules', 'bahin', 'sa', 'maong', 'pagrehistro', 'sa', 'Disyembre', '27', 'karong', 'tuiga.', 'Gi-isyu', 'sa', 'National', 'Telecommunications', 'Commission', '(', 'NTC', ')', 'ang', 'implementing', 'rules', 'alang', 'sa', 'SIM', 'Registration', 'Act', 'niadtong', 'Lunes', ',', 'Disyembre', '12', ',', '2022.', 'Ang', 'maong', 'balaod', 'nag-require', 'sa', 'tanang', 'mobile', 'subscribers', 'sa', 'pagrehistro', 'sa', 'ilang', 'SIM', 'cards', 'sulod', 'sa', '180', 'ka', 'adlaw', 'o', 'unom', 'ka', 'bulan', 'gikan', 'sa', 'pag-epekto', 'sa', 'maong', 'balaod', 'o', 'mag-atubang', 'og', 'automatic', 'deactivation', 'ang', 'ilang', 'mga', 'cards.', 'Mahimong', 'ma-reactivate', 'kini', 'apan', 'sulod', 'lamang', 'sa', 'lima', 'ka', 'adlaw', ',', 'matod', 'pa', 'sa', 'balaod.', 'Buhaton', 'ang', 'SIM', 'registration', 'pinaagi', 'sa', 'usa', 'ka', 'secure', 'platform', 'o', 'website', 'nga', 'ihatag', 'sa', 'telecommunications', 'company.', 'Kinahanglan', 'nga', 'mohatag', 'ang', 'individual', 'users', 'sa', 'ilang', 'full', 'name', ',', 'date', 'of', 'birth', ',', 'sex', ',', 'address', 'ug', 'valid', 'government', 'ID', 'o', 'susamang', 'mga', 'dokumento', 'nga', 'aduna'y', 'litrato', ',', 'samtang', 'ang', 'business', 'users', 'kinahanglang', 'mohatag', 'sa', 'business', 'name', ',', 'business', 'address', 'ug', 'full', 'name', 'sa', 'authorized', 'signatory.', 'Ang', 'mga', 'foreigners', ',', 'kinahanglan', 'nga', 'mohatag', 'sa', 'ilang', 'personal', 'data', 'apil', 'na', 'sab', 'ang', 'passport', 'information', 'ug', 'ilang', 'address.', 'Isyuhan', 'og', 'SIM', 'card', 'nga', 'valid', 'sa', '30', 'ka', 'adlaw', 'ang', 'mga', 'nibisita', 'sa', 'nasud', 'isip', 'turista', ',', 'diin', 'mahimo', 'kining', 'i-extend', 'sa', 'dihang', 'mopresentar', 'sila', 'sa', 'ilang', 'approved', 'visa', 'extension.', 'Ang', 'mga', 'foreigners', 'nga', 'aduna'y', 'ubang', 'klase', 'sa', 'visa', 'mahimong', 'makakuha', 'sa', 'SIM', 'card', 'nga', 'wala'y', '30-day', 'validity', 'period.', '#', 'NewsBite', '|', 'with', 'reports', 'from', 'INQUIRER.net'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,383
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Sigurado', 'nga', 'malipayon', 'gyud', 'ang', 'imong', 'Pasko', 'kung', 'ikaw', 'ang', 'mosunod', 'nga', 'mahimong', 'milyonaryo', '!', 'Wala', 'pa', 'gihapon', 'nakadaog', 'sa', 'kapin', 'P390', 'milyon', 'nga', 'jackpot', 'prize', 'sa', 'Ultra', 'Lotto', '6', '/', '58', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0]
|
cebuaner
|
4,384
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BORACAY', 'DELIKADO', 'NGA', 'MAHUGNO', 'TUNGOD', 'SA', 'NAKIT-AN', 'NGA', '815', 'KA', 'SINKHOLES', 'Nipasidaan', 'ang', 'Department', 'of', 'Environment', 'and', 'Natural', 'Resources-Mines', 'and', 'Geoscience', 'Bureau', '(', 'DENR-MGB', ')', 'nga', 'posibleng', 'mahugno', 'ang', 'Boracay', 'Island', 'human', 'nadiskubre', 'ang', '815', 'ka', 'sinkholes.', 'Sumala', 'pa', 'ni', 'Magarzo', ',', 'nagkadaghan', 'ang', 'sinkholes', 'sa', 'pinakasikat', 'nga', 'beach', 'destination', 'sa', 'nasud', 'sa', 'Malay', 'town', ',', 'Aklan', 'sa', 'nilabay', 'nga', 'upat', 'ka', 'tuig.', 'Nakit-an', 'sa', 'MGB-6', 'ang', '789', 'ka', 'sinkholes', 'sa', 'tulo', 'ka', 'mga', 'barangay', 'atol', 'sa', 'hazard', 'assessment', 'niadtong', '2018.', 'Mao', 'kini', 'ang', 'tuig', 'diin', 'gipatuman', 'sa', 'Duterte', 'administration', 'ang', 'massive', 'rehabilitation', 'ug', 'pag-ban', 'sa', 'mga', 'turista', 'sa', 'Boracay', 'sulod', 'sa', 'unom', 'ka', 'bulan.', 'Niadtong', '2019', ',', 'nisaka', 'ang', 'sinkholes', 'ngadto', 'sa', '801.', 'Karong', '2022', ',', 'nisaka', 'na', 'sab', 'kini', 'sa', '815.', 'Gisubli', 'ni', 'Magarzo', 'nga', 'delikado', 'ang', 'Boracay', 'sa', 'sinkholes', 'tungod', 'ang', 'yuta', 'niini', 'ginama', 'kasagaran', 'sa', 'limestone.', 'Dugang', 'pa', 'niya', ',', 'lisod', 'nga', 'matagna', 'ang', 'sinkholes', 'tungod', 'kalit', 'lang', 'kini', 'nga', 'mahitabo', 'ug', 'wala', 'kini', 'mga', 'paunang', 'timailhan.', 'Nakita', 'sab', 'sa', 'MGB-6', 'Coastal', 'Geohazard', 'Map', 'ang', 'mga', 'key', 'areas', 'sa', 'resort-island', 'nga', 'delikado', 'sa', 'erosion', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 1, 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, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,385
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['US', 'RESEARCHERS', 'GIANUNSYO', 'ANG', 'MAKASAYSAYANONG', 'KALAMPUSAN', 'SA', 'NUCLEAR', 'FUSION', 'Gianunsyo', 'sa', 'mga', 'US', 'researchers', 'ang', 'makasaysayanong', 'kalampusan', 'sa', 'nuclear', 'fusion', 'niadtong', 'Martes.', 'Gipahibalo', 'nila', 'nga', 'usa', 'ka', 'federal', 'research', 'facility', 'ang', 'nakab-ot', 'ang', 'fusion', 'ignition', ',', 'diin', 'mahimong', 'makatabang', 'kini', 'sa', 'paghatag', 'og', 'mga', 'alternatibong', 'clean', 'energy', 'sources.', 'Sumala', 'pa', 'sa', 'Lawrence', 'Livermore', 'National', 'Laboratory', ',', 'gihimo', 'ang', 'maong', 'eksperimento', 'aron', '"', 'produced', 'more', 'energy', 'from', 'fusion', 'than', 'the', 'laser', 'energy', 'used', 'to', 'drive', 'it.', '"', 'Gihulagway', 'sa', 'US', 'Department', 'of', 'Energy', 'ang', 'kalampusan', 'sa', 'fusion', 'ignition', 'isip', '"', 'major', 'scientific', 'breakthrough', '"', 'nga', 'modala', 'ngadto', 'sa', '"', 'advancements', 'in', 'national', 'defense', 'and', 'the', 'future', 'of', 'clean', 'power.', '"', 'Gihulagway', 'sab', 'kini', 'ni', 'LLNL', 'director', 'Dr.', 'Kim', 'Budil', 'isip', '"', 'one', 'of', 'the', 'most', 'significant', 'scientific', 'challenges', 'ever', 'tackled', 'by', 'humanity.', '"', 'Nagtrabaho', 'ang', 'mga', 'siyentista', 'sulod', 'sa', 'mga', 'dekada', 'aron', 'pagpalambo', 'sa', 'nuclear', 'fusion', 'nga', 'gipasidunggan', 'sa', 'mga', 'nagsuporta', 'isip', '"', 'clean', ',', 'abundant', 'and', 'safe', 'source', 'of', 'energy', 'that', 'could', 'eventually', 'allow', 'humanity', 'to', 'break', 'its', 'dependence', 'on', 'the', 'fossil', 'fuels', 'driving', 'a', 'global', 'climate', 'crisis', '.', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,386
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakuha', 'sa', 'NASA', 'ang', 'usa', 'ka', ''colorful', ''', 'nga', 'hulagway', 'nga', 'resulta', 'sa', 'supernova', 'explosion', 'sa', 'usa', 'ka', 'dakong', 'bituon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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]
|
cebuaner
|
4,387
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakadawat', 'og', 'cash', 'incentives', 'gikan', 'sa', 'Silliman', 'University', 'ang', 'mga', 'top-notchers', 'nga', 'sila', 'si', 'Angela', 'Claire', 'N.', 'Kitane', 'ug', 'Amari', 'Joy', 'O.', 'Samson', ',', 'kinsa', 'anaa', 'sa', 'rank', '5th', 'ug', '10th', 'sa', 'November', '2022', 'Nurse', 'Licensure', 'Examination', '(', 'NLE', ')', '.', 'Nakadawat', 'sila', 'sa', 'tseke', 'sa', 'usa', 'ka', 'turnover', 'ceremony', 'uban', 'nila', 'ni', 'Prof.', 'Jane', 'Annette', 'Belarmino', ',', 'SU', 'vice', 'president', 'for', 'Development', ',', 'Enterprises', ',', 'and', 'External', 'Affairs', ',', 'niadtong', 'December', '12', ',', '2022', 'sa', 'Administration', 'board', 'room.', 'Gitagaan', 'sa', 'SU', 'og', 'awards', 'nga', 'cash', 'incentives', 'ang', 'mga', 'ni-graduate', 'kinsa', 'nakakuha', 'og', 'spot', 'sa', 'top', 'examinees', 'sa', 'licensure', 'exams.', 'Gianunsyo', 'sa', 'Professional', 'Regulation', 'Commission', '(', 'PRC', ')', 'ang', 'mga', 'resulta', 'sa', 'November', '2022', 'NLE', 'niadtong', 'Nobyembre', '30', ',', '2022', ',', 'diin', 'ang', 'SU', 'anaa'y', 'tulo', 'ka', 'top-notchers', 'kinsa', 'anaa', 'sa', 'rank', '5th', ',', '9th', ',', 'ug', '10th.', 'Anaa', 'sab', 'sa', 'rank', '1', 'ang', 'SU', 'sa', 'lista', 'sa', 'mga', 'top', 'schools', 'alang', 'sa', 'November', '2022', 'Nurse', 'Licensure', 'Examination', '(', 'NLE', ')', 'uban', 'sa', '100', '%', 'passing', 'rate', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 2, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,388
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CEBU', 'PACIFIC', 'NAGTANYAG', 'OG', 'P12', 'NGA', 'PLITE', 'HANGTOD', 'DISYEMBRE', '14', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'P12', 'nga', 'plite', 'alang', 'sa', 'mga', 'biyahero', 'sa', 'mga', 'pinili', 'nga', 'domestic', 'o', 'international', 'spots.', 'Lakip', 'sa', 'mga', 'local', 'destinations', 'nga', 'maadto', 'gikan', 'sa', 'Manila', 'mao', 'ang', 'Bohol', ',', 'Boracay', ',', 'Siargao', ',', 'ug', 'Palawan.', 'Apil', 'sa', '12.12', 'nga', 'deal', 'ang', 'international', 'spots', 'sama', 'sa', 'Hong', 'Kong', ',', 'Tokyo', ',', 'Osaka', ',', 'Hanoi', ',', 'Bali', ',', 'Jakarta', ',', 'Bangkok', ',', 'ug', 'daghan', 'pa', 'alang', 'sa', 'susamang', 'one-way', 'base', 'fare', 'gikan', 'sa', 'Manila.', 'Sumala', 'pa', 'sa', 'CEB', ',', 'ang', 'gi-quote', 'nga', 'international', 'fares', 'mao', 'ang', '"', 'inclusive', 'of', '7', 'kg', 'hand-carry', 'baggage', 'allowance', ',', 'but', 'exclusive', 'of', 'web', 'admin', 'fee', 'for', 'short', 'haul', 'and', 'long', 'haul', 'flights', ',', 'respectively', ',', 'and', 'P550', 'international', 'terminal', 'fee', 'and', 'fuel', 'surcharge.', '"', 'Mahimong', 'mo-avail', 'sa', 'maong', 'promo', 'hangtod', 'Disyembre', '14', ',', 'ug', 'aduna', 'kini', 'travel', 'period', 'gikan', 'Hunyo', '1', 'hangtod', 'Nobyembre', '30', ',', '2023.', 'Aron', 'pag-book', 'sa', 'biyahe', ',', 'bisitaha', 'ang', 'seat', 'sale', 'section', 'sa', 'official', 'website', 'sa', 'CEB', 'ug', 'i-click', 'ang', '“book', 'now”', 'sa', 'kilid', 'sa', 'napili', 'nga', 'destinasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,389
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'swerteng', 'nipatad', 'ang', 'nakadaog', 'sa', 'jackpot', 'nga', 'mobalor', 'og', 'kapin', 'P23', 'milyon', 'human', 'na-draw', 'ang', 'winning', 'combination', 'sa', 'Super', 'Lotto', '6', '/', '49', 'niadtong', 'Dominggo', ',', 'Disyembre', '11', ',', '2022', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,390
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'hulagway', 'sa', 'Christmas', 'decorations', 'sa', 'Department', 'of', 'Public', 'Works', 'and', 'Highways', '(', 'DPWH', ')', '-', '2nd', 'District', 'sa', 'Cangmating', ',', 'Sibulan.', 'Abri', 'kini', 'alang', 'sa', 'publiko', 'gikan', '6-10pm', 'sugod', 'niadtong', 'Dec.', '6', ',', '2022', 'hangtod', 'Jan.', '6', ',', '2023', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,391
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'Dumagueteño', ',', 'nagtigom', 'na', 'karon', 'sa', 'Perdices', 'Coliseum', '(', 'Oval', ')', 'ning', 'dakbayan', 'karong', 'adlawa', 'alang', 'sa', '#', 'TMFunPasko', 'party.', 'Libre', 'ang', 'maong', 'concert', 'ug', 'panguluhan', 'kini', 'sa', 'SB19', ',', 'The', 'Juans', ',', 'Alden', 'Richards', ',', 'Albert', 'Nicolas', ',', 'KAIA', ',', 'Matthaios', ',', '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, 7, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 4, 0, 1, 2, 0, 1, 2, 0, 3, 0, 3, 0, 0, 0, 0, 0]
|
cebuaner
|
4,392
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpamatuod', 'nga', 'smash', 'hit', 'ang', 'anime', 'film', 'nga', '#', 'TheFirstSlamDunk', 'diin', 'nakakuha', 'kini', 'og', 'second', 'highest', 'first', 'day', 'gross', 'sa', 'Japan', 'ug', 'mao', 'ang', '#', '1', 'nga', 'salida', 'sa', 'pagkakaron.', 'Ipagawas', 'kini', 'sa', 'Pilipinas', 'karong', 'Pebrero', '1', ',', '2023', 'diin', 'malantaw', 'ang', 'exciting', 'Inter-High', 'performance', 'ni', 'Hanamichi', 'Sakuragi', 'ug', 'ang', 'Shohoku', 'High', 'School', 'kontra', 'sa', 'top', 'teams', 'sa', 'Japan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 3, 4, 4, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,393
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['80', 'KA', 'SAKAYAN', 'SA', 'PANGISDA', ',', 'GIAPOD-APOD', 'ALANG', 'SA', '240', 'KA', 'MGA', 'LOKAL', 'NGA', 'MANGINGISDA', 'Giapod-apod', 'ang', '80', 'ka', 'fiberglass', 'motorized', 'fishing', 'boats', 'alang', 'sa', '240', 'ka', 'mga', 'lokal', 'nga', 'mangingisda', 'ug', 'ilang', 'pamilya', 'atol', 'sa', 'usa', 'ka', 'simple', 'nga', 'seremonya', 'sa', 'Pantawan', '2', 'Rizal', 'Boulevard', 'kagahapong', 'adlawa', ',', 'Disyembre', '8', ',', '2022.', 'Gipangunahan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ang', 'distribusyon', 'ug', 'giawhag', 'ang', 'miyembro', 'sa', '12', 'ka', 'fisherfolk', 'associations', 'sa', 'Barangay', 'Bantayan', ',', 'Piapi', ',', 'Looc', ',', 'Poblacion', '1', '(', 'Tinago', ')', ',', 'Calindagan', ',', 'Mangnao', 'ug', 'Banilad', 'nga', 'ampingan', 'ang', 'ilang', 'fishing', 'boats', 'aron', 'molungtad', 'og', 'dugay', 'ug', 'makatabang', 'sa', 'ilang', 'tagsa-tagsa', 'ka', 'mga', 'pamilya', 'nga', 'madugangan', 'ang', 'kita.', 'Makatabang', 'ang', 'maong', 'proyekto', 'sa', 'mga', 'lokal', 'nga', 'mangingisda', 'aron', 'makadakop', 'og', 'first', 'class', 'nga', 'mga', 'isda', ',', 'makahatag', 'og', 'lab-as', 'ug', 'barato', 'nga', 'suplay', 'sa', 'isda', 'aron', 'masuportaan', 'ang', 'mga', 'restaurant', 'ug', 'hotel', 'ug', 'makamugna', 'og', 'trabaho.', 'Ang', 'matag', 'sakayan', ',', 'mahimong', 'magamit', 'sa', 'usa', 'ka', 'grupo', 'nga', 'aduna'y', 'tulo', 'ka', 'mangingisda.', 'Aduna'y', '80', 'ka', 'mga', 'unit', 'nga', 'magamit', 'sa', 'kinatibuk-ang', '240', 'ka', 'mga', 'benepisyaryo.', 'Girehistro', 'og', 'una', 'ang', 'tanang', 'fishing', 'boats', 'sa', 'wala', 'pa', 'kini', 'giapod-apod', 'sa', 'Philippine', 'Coast', 'Guard', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 5, 6, 0, 5, 0, 5, 6, 6, 6, 6, 6, 6, 0, 5, 0, 5, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,394
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gihaya', 'na', 'karon', 'ang', 'patayng', 'lawas', 'sa', 'inilang', 'singer', 'nga', 'si', 'Jovit', 'Baldivino', 'sa', 'iyang', 'panimalay', 'sa', 'Rosario', ',', 'Batangas.', 'Nitaliwan', 'ganinang', 'kadlawon', 'si', 'Baldivino', 'sa', 'edad', 'nga', '29', 'anyos', 'tungod', 'sa', 'mga', 'komplikasyon', 'nga', 'dala', 'sa', 'stroke', ',', 'sumala', 'pa', 'sa', 'iyang', 'banay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 2, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,395
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitaliwan', 'na', 'sa', 'laing', 'kalibutan', 'ang', 'inilang', 'singer', 'nga', 'si', 'Jovit', 'Baldivino', 'karong', 'adlawa', ',', 'Dec.', '9', ',', '2022', ',', 'matud', 'pa', 'sa', 'iyang', 'pamilya.', 'Sumala', 'sa', 'iyang', 'igsuon', 'nga', 'si', 'Gil', ',', 'nipanaw', 'si', 'Jovit', 'ganinang', 'kadlawon', 'sa', 'usa', 'ka', 'tambalanan', 'sa', 'Batangas', 'sa', 'edad', 'nga', '29', 'anyos.', 'Aneurysm', 'ang', 'gitumbok', 'nga', 'rason', 'sa', 'iyang', 'kamatayon.', 'Naila', 'si', 'Baldivino', 'isip', 'kinaunahang', 'mananaug', 'sa', 'Pilipinas', 'Got', 'Talent', 'sa', 'ABS-CBN', 'niadtong', '2010', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 4, 4, 0, 3, 0, 0, 0]
|
cebuaner
|
4,396
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ASTRONAUT', 'FOOD', ''', 'O', 'FOOD', 'PILL', 'ALANG', 'SA', 'MGA', 'KABUS', ',', 'NAHISGUTAN', 'SA', 'CONFIRMATION', 'HEARING', 'SA', 'DOST', 'CHIEF', 'Nisantop', 'sa', 'hunahuna', 'sa', 'usa', 'ka', 'magbabalaod', 'ang', 'pagpakaon', 'og', 'food', 'pills', 'ngadto', 'sa', 'labing', 'kabus', 'nga', 'mga', 'Pilipino', 'diin', 'molungtad', 'kini', 'og', 'mga', 'adlaw', 'o', 'bulan', 'sama', 'sa', 'gikaon', 'sa', 'mga', 'astronaut', 'sa', 'kawanangan.', 'Apan', 'mahimo', 'kini', 'kung', 'makaimbento', 'ang', 'mga', 'siyentistang', 'Pilipino', 'sa', 'maong', 'klase', 'sa', 'pagkaon.', 'Nahisgutan', 'ni', 'Sagip', 'Partylist', 'Representative', 'Rodante', 'Marcoleta', 'ang', 'maong', 'ideya', 'niadtong', 'Miyerkules', 'atol', 'sa', 'confirmation', 'hearing', 'ni', 'Department', 'of', 'Science', 'and', 'Technology', 'Secretary', 'Renato', 'Umali', 'Solidum', 'Jr.', 'Gisubli', 'ni', 'Marcoleta', 'nga', 'naka-survive', 'ang', 'mga', 'astronouts', 'sa', 'kawangan', 'nga', 'wala', 'nagluto', 'og', 'pagkaon', 'didto', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,397
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGROS', 'ORIENTAL', 'UG', 'KABISAY-AN', 'MAKASINATI', 'OG', 'PAG-ULAN', 'TUNGOD', 'SA', 'TROUGH', 'SA', 'LPA', 'Nipagawas', 'og', 'Regional', 'Weather', 'Forecast', 'ang', 'PAGASA', 'Visayas', 'PRSD', 'mga', 'alas-5', 'sa', 'buntag', 'karong', 'adlawa', ',', 'Disyembre', '8', ',', '2022.', 'Makasinati', 'ang', 'Visayas', 'og', 'madag-umon', 'nga', 'kalangitan', 'ug', 'matag', 'karon', 'og', 'unya', 'nga', 'pag-ulan', ',', 'pagdalugdog', ',', 'ug', 'pagpangilat', 'tungod', 'sa', 'trough', 'sa', 'Low', 'Pressure', 'Area', '(', 'LPA', ')', '.', 'Medyo', 'madag-umon', 'nga', 'kalangitan', 'ug', 'ginagmay', 'nga', 'pag-ulan', 'ug', 'pagdalugdog', 'ang', 'masinati', 'sa', 'Palawan', 'ug', 'Occidental', 'Mindoro', 'tungod', 'sa', 'localized', 'thunderstorms.', 'Masinati', 'sab', 'sa', 'Visayas', ',', 'Palawan', ',', 'ug', 'Occidental', 'Mindoro', 'ang', 'hinay', 'ngadto', 'sa', 'kasarangan', 'nga', 'paghuros', 'sa', 'hangin', 'gikan', 'sa', 'Amihanang-Silangan', 'ngadto', 'sa', 'Amihanang-Kasadpan', 'diin', 'ang', 'kadagatan', 'aduna'y', 'hinay', 'ngadto', 'sa', 'kasarangan', 'nga', 'pagbalud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 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, 0, 0, 0, 0, 0, 0, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,398
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nag-andam', 'na', 'alang', 'sa', '#', 'TMFunPasko', 'karong', 'December', '10', ',', '3:00pm', ',', 'sa', 'Perdices', 'Coliseum', '(', 'Oval', ')', ',', 'Kagawasan', 'Freedom', 'Park', 'Avenue.', 'Maki-party', 'sila', 'si', 'Alden', 'Richards', ',', 'Andrea', 'Brillantes', ',', 'SB19', ',', 'The', 'Juans', ',', 'Kaia', ',', 'Matthaios', ',', 'Seth', 'Fedelin', ',', 'Maris', ',', 'Baninay', 'Bautista', ',', 'Albert', 'Nicolas', ',', 'Arshie', 'Larga', 'ug', 'Gaia', 'Poly', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 1, 2, 0, 1, 2, 0, 3, 0, 3, 4, 0, 3, 0, 1, 0, 1, 2, 0, 1, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0]
|
cebuaner
|
4,399
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nakakuha', 'og', 'atensyon', 'ang', ''unique', ''', 'nga', 'Christmas', 'tree', 'sa', 'Tagoloan', 'Municipal', 'Police', 'Station', 'sa', 'Tagoloan', ',', 'Misamis', 'Oriental', 'nga', 'gikan', 'sa', 'mga', 'impounded', 'motor', 'vehicles.', 'Sa', 'maong', 'Christmas', 'tree', ',', 'aduna'y', 'pedicab', 'sa', 'ibabaw', 'niini', 'nga', 'nagsilbing', 'parol.', 'Gihimoan', 'sab', 'kini', 'og', 'belen', 'sa', 'tiilan', '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, 7, 0, 0, 7, 8, 8, 8, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
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