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4,200
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What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GRADE', '3', 'STUDENT', 'SA', 'BACONG', ',', 'GIINGONG', 'HAPIT', 'MA-KIDNAP', 'Gibutyag', 'sa', 'Bacong', 'Central', 'School', '(', 'BCS', ')', 'nga', 'hapit', 'kuno', 'madagit', 'ang', 'usa', 'ka', 'tinun-an', 'niini', 'duol', 'sa', 'gate', 'sa', 'eskuwelahan', 'bag-uhay', 'lamang.', 'Sumala', 'pa', 'sa', 'magtutudlo', 'sa', 'BCS', 'nga', 'si', 'Bridget', 'Calijan', ',', 'pasado', 'alas-12', 'sa', 'udto', 'niadtong', 'Mar.', '1', 'nahitabo', 'ang', 'insidente.', 'Naglakaw', 'kuno', 'pagawas', 'sa', 'gate', 'ang', 'usa', 'ka', 'Grade', '3', 'nga', 'estudyante', 'sa', 'eskuwelahan', 'sa', 'dihang', 'dunay', 'usa', 'ka', 'gulang', 'nga', 'lalaki', 'nga', 'naka-mask', 'nga', 'nagtindog', 'sa', 'dapit.', 'Giingong', 'kalit', 'nga', 'gigunit', 'sa', 'lalaki', 'ang', 'estudaynte', 'og', 'giagda', 'kini', 'nga', 'mokuyog', 'niya', 'aron', 'mopalit', 'og', 'ice', 'cream.', 'Daling', 'nakasinggit', 'ang', 'estudyante', 'ug', 'nangayo', 'kini', 'og', 'tabang.', 'Maayo', 'na', 'lang', 'kay', 'dunay', 'usa', 'ka', 'ginikanan', 'nga', 'niduol', 'og', 'nisulay', 'pagbira', 'sa', 'bata', 'gikan', 'sa', 'wala', 'pa', 'mailhing', 'lalaki.', 'Karong', 'semanaha', 'lamang', 'kini', 'gibutyag', 'sa', 'BCS', 'sa', 'ilang', 'Facebook', 'post', ',', 'human', 'nga', 'ma-report', 'ang', 'insidente', 'ngadto', 'sa', 'kapulisan', 'sa', 'lungsod.', 'Tungod', 'sa', 'maong', 'insidente', ',', 'nanawagan', 'ang', 'kadagkuan', 'sa', 'BCS', 'nga', 'tambagan', 'sa', 'mga', 'ginikanan', 'ang', 'ilang', 'mga', 'anak', 'paglikay', 'sa', 'paggawas', 'sa', 'gate', 'nga', 'mag-inusara.', 'Kini', 'alang', 'pud', 'sa', 'ilang', 'kaluwasan', ',', 'sumala', 'pa', 'sa', 'eskuwelahan.', '"', 'Palihog', 'mga', 'parents', 'tabang', 'intawon', 'mo', 'ug', 'tambag', 'sa', 'inyo', 'mga', 'kabataan', 'nga', 'dili', 'gyud', 'pasagad', 'ug', 'gawas', 'sa', 'gate', 'ug', 'wala', 'pa', 'silay', 'mga', 'sundo', 'kay', 'para', 'safety', 'sila', ',', '"', 'ingon', 'pa', 'sa', 'eskuwelahan', 'sa', 'Facebook', 'post', '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.
|
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|
cebuaner
|
4,201
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gibalhin', 'na', 'sa', 'Negros', 'Oriental', 'Provincial', 'Capitol', 'ning', 'dakbayan', 'ang', 'patayng', 'lawas', 'ni', 'Gov.', 'Roel', 'Degamo.', 'Gitakdang', 'ihaya', 'kini', 'sa', 'Kapitolyo', 'hangtod', 'karong', 'Domingo', ',', 'Marso', '12.', 'Sunod', 'semana', ',', 'gitakdang', 'iuli', 'ang', 'iyang', 'patayng', 'lawas', 'sa', 'Bonawon', ',', 'Siaton', ',', 'ug', 'ilubong', 'kini', 'tapad', 'sa', 'iyang', 'mga', 'ginikanan', 'ug', 'igsuong', 'babaye', ',', 'sumala', 'pa', 'sa', 'Kapitolyo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 6, 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,202
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MS.', 'NEGROS', 'ORIENTAL', 'ANGEL', 'SAGA', ',', 'NANAWAGAN', 'OG', 'HUSTISYA', 'HUMAN', 'ANG', 'PAGPATAY', 'KANG', 'GOV.', 'DEGAMO', 'Gipanawagan', 'ni', 'Miss', 'Negros', 'Oriental', '2022', 'Angel', 'Saga', 'ang', 'hustisya', 'alang', 'sa', 'tibuok', 'Negros', 'Oriental', 'human', 'ang', 'pagpamusil-patay', 'kang', 'Gov.', 'Roel', 'Degamo', 'ug', 'sa', '8', 'pa', 'ka', 'biktima.', 'Dugang', 'pa', 'niya', ',', 'tumong', 'niya', 'nga', 'magbarug', 'alang', 'sa', 'kalinaw', 'sa', 'probinsya', 'subay', 'sa', 'iyang', 'responsibilidad', 'isip', 'goodwill', 'ambassador', '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.
|
[3, 4, 4, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 4, 4, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 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]
|
cebuaner
|
4,203
|
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', 'usa', 'ka', '"', 'colorized', 'photo', '"', 'sa', 'mga', 'lokal', 'kinsa', 'nag-posing', 'samtang', 'nagsaulog', 'sa', 'pagtapos', 'sa', 'World', 'War', 'II.', 'Nakuha', 'kini', 'nga', 'litrato', 'sa', 'usa', 'ka', 'dapit', 'sa', 'lungsod', 'sa', 'Tanjay', 'sa', 'Negros', 'Oriental', 'niadtong', '1946', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0]
|
cebuaner
|
4,204
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MESSENGER', 'APP', ',', 'IBALIK', 'NA', 'PAG-USA', 'SA', 'FACEBOOK', 'Gikatahong', 'i-usa', 'na', 'pagbalik', 'ang', 'Messenger', 'app', 'sa', 'Facebook', 'human', 'kini', 'gianunsyo', 'sa', 'kadagkuan', 'sa', 'Meta', 'karong', 'semanaha.', 'Kini', 'human', 'ang', 'dul-an', 'usa', 'ka', 'dekada', 'nga', 'magkalahi', 'ang', 'duha', 'ka', 'app.', 'Mahinumduman', 'nga', 'magkauban', 'ang', 'Facebook', 'Chats', 'sa', 'Facebook', 'app', 'hangtud', 'niadtong', '2014', ',', 'human', 'namugna', 'ang', 'Messenger', 'ug', 'gibulag', 'kini', 'sa', 'Facebook.', 'Ikaw', ',', 'beshie', ',', 'sugot', 'ba', 'ka', 'nga', 'ibalik', 'na', 'sab', 'ang', 'Messenger', 'sa', 'Facebook', '?'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 7, 0, 0, 0, 0, 0, 7, 8, 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, 3, 4, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0]
|
cebuaner
|
4,205
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Daw', 'wala', 'matandog', 'ang', 'opisina', 'ni', 'Gov.', 'Roel', 'Degamo', 'sa', 'Kapitolyo', 'sa', 'Negros', 'Oriental', ',', 'pipila', 'ka', 'adlaw', 'human', 'siya', 'gipusil', 'patay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,206
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Napuno', 'og', 'mga', 'mensahe', 'sa', 'pagbangutan', 'ug', 'pagduyog', 'ang', 'dedication', 'wall', 'alang', 'kang', 'Gov.', 'Roel', 'Degamo', 'sa', 'gawas', 'sa', 'Kapitolyo', 'karong', 'buntag', ',', 'Mar.', '9', ',', '2023.', 'Gitakdang', 'dad-on', 'sa', 'Kapitolyo', 'karong', 'adlawa', 'ang', 'patayng', 'lawas', 'sa', 'napatay', 'nga', 'gobernador', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,207
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Mga', 'dugang', 'hulagway', 'sa', 'pagbisita', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'sa', 'haya', 'ni', 'Gov.', 'Roel', 'Degamo', 'sa', 'Barangay', 'Junob', ',', 'Dumaguete', 'City', 'karong', 'gabii', ',', 'Mar.', '8', ',', '2023.', 'Nakigstorya', 'si', 'Marcos', 'sa', 'biyuda', 'ni', 'Degamo', 'nga', 'si', 'Pamplona', 'Mayor', 'Janice', 'Degamo', ',', 'ingon', 'man', 'sa', 'mga', 'kabanay', 'sa', '8', 'ka', 'biktima', 'nga', 'napatay', 'kauban', 'sa', 'kanhing', 'gobernador.', 'Nipasalig', 'ang', 'presidente', 'nga', 'makab-ot', 'ang', 'hustisya', 'sa', 'pagkamatay', 'ni', 'Degamo', ',', 'ingon', 'man', 'ayuda', 'alang', 'sa', 'mga', 'nagbangotan', 'nga', 'kabanay', 'sa', 'uban', 'pang', 'mga', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 1, 2, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,208
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giduyogan', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'nagbangotang', 'biyuda', 'ni', 'Gov.', 'Roel', 'Degamo', 'nga', 'si', 'Pamplona', 'Mayor', 'Janice', 'Degamo', 'sa', 'dihang', 'nibisita', 'siya', 'sa', 'haya', 'sa', 'iyang', 'suod', 'nga', 'kaalyado', 'sa', 'Barangay', 'Junob', ',', 'Dumaguete', 'City', 'karong', 'gabii', ',', 'Mar.', '8', ',', '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, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 5, 0, 1, 2, 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]
|
cebuaner
|
4,209
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nibisita', 'karong', 'gabii', ',', 'Mar.', '8', ',', '2023', ',', 'si', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'sa', 'haya', 'sa', 'iyang', 'suod', 'nga', 'kaalyado', 'nga', 'si', 'Gov.', 'Roel', 'Degamo', 'sa', 'Barangay', 'Junob', ',', 'Dumaguete', 'City.', 'Mao', 'kini', 'ang', 'unang', 'higayon', 'nga', 'nibisita', 'si', 'Marcos', 'sa', 'Negros', 'Oriental', 'sukad', 'siya', 'nahimong', 'presidente', 'sa', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,210
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DOJ', ':', 'MASTERMIND', 'SA', 'PAGPATAY', 'KANG', 'GOV.', 'DEGAMO', ',', 'HAPIT', 'NA', 'MATINO', 'Gibutyag', 'ni', 'Justice', 'Secretary', 'Jesus', 'Crispin', 'Remulla', 'karong', 'Miyerkoles', '(', 'Mar.', '8', ',', '2023', ')', 'nga', 'hapit', 'na', 'nilang', 'matino', 'kung', 'kinsa', 'ang', 'mastermind', 'sa', 'pagpatay', 'kang', 'Gov.', 'Roel', 'Degamo', 'ug', '8', 'uban', 'pa.', 'Kini', 'human', 'giambit', 'sa', 'mga', 'giingong', 'gunmen', 'ni', 'Degamo', 'ang', 'video', 'recording', 'diin', 'nakigstorya', 'kuno', 'sila', 'sa', 'naasoy', 'nga', 'mastermind.', 'Hinuon', ',', 'wala', 'na', 'naghatag', 'og', 'dugang', 'detalye', 'si', 'Remulla', 'kabahin', 'kung', 'kinsa', 'ang', 'maong', 'mastermind', 'og', 'kung', 'unsay', 'gistoryahan', 'sa', 'mga', 'suspek', 'sa', 'giigngong', 'video.', 'Dugang', 'pa', 'sa', 'kalihim', ',', 'hapit', 'na', 'kuno', 'mahuman', 'og', 'sulbad', 'ang', 'pagpatay', 'kang', 'Degamo', 'human', 'nga', 'mabasa', 'niya', 'ang', 'mga', 'pamahayag', 'sa', 'mga', 'suspek.', 'Ang', 'kulang', 'na', 'lang', ',', 'matud', 'ni', 'Remulla', ',', 'mao', 'ang', 'pagdakop', 'sa', 'uban', 'pang', 'mga', 'suspek', 'nga', 'at', 'large', 'gihapon', 'sa', 'pagkakaron', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
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|
cebuaner
|
4,211
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KA', 'SUSPEK', 'SA', 'PAGPATAY', 'KANG', 'GOV.', 'DEGAMO', ',', 'GIPAUBOS', 'SA', 'WITNESS', 'PROTECTION', 'PROGRAM', 'Gipailalom', 'sa', 'witness', 'protection', 'program', 'ang', 'duha', 'sa', 'upat', 'ka', 'mga', 'suspek', 'nga', 'nasikop', 'sa', 'manhunt', 'operation', 'sa', 'Special', 'Investigation', 'Task', 'Group', 'human', 'sa', 'pagpatay', 'ni', 'kanhi', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'ug', 'apil', 'na', 'ang', 'laing', 'walo', 'pa', 'ka', 'indibidwal.', 'Apan', ',', 'wala', 'gibutyag', 'sa', 'tigpamaba', 'sa', 'maong', 'task', 'group', 'nga', 'si', 'Police', 'Lt.', 'Col.', 'Gerard', 'Ace', 'Pelare', 'kung', 'kinsa', 'sa', 'upat', 'ang', 'state', 'witnesses.', 'Ang', 'upat', 'ka', 'mga', 'nasikop', 'nga', 'suspek', 'mao', 'sila', 'si', 'Joric', 'Labrador', ',', '50', 'anyos', ',', 'usa', 'ka', 'ex-army', ',', 'lumolupyo', 'sa', 'Cagayan', 'De', 'Oro', ';', 'Joven', 'Aber', ',', '42', 'anyos', ',', 'usa', 'ka', 'ex-army', 'ranger', ',', 'residente', 'sa', 'Barangay', 'Robles', ',', 'La', 'Castellana', ',', 'Negros', 'Occidental', ';', 'Benjie', 'Rodriguez', ',', '45', 'anyos', ',', 'lumad', 'nga', 'taga', 'Mindanao', ';', 'ug', 'Osmundo', 'Rivero', ',', 'usa', 'ka', 'kanhi', 'sundalo', 'sa', 'Zamboanga', 'City.', 'Nag-atubang', 'sila', 'og', 'multiple', 'murder', ',', 'multiple', 'frustrated', 'murder', 'ug', 'illegal', 'possession', 'of', 'firearms', 'and', 'explosives', 'human', 'sa', 'kamatayon', 'ni', 'Degamo', 'ug', 'walo', 'pa', 'ka', 'indibidwal', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
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|
cebuaner
|
4,212
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nibisita', 'sab', 'sa', 'haya', 'ni', 'Gov.', 'Roel', 'Degamo', 'sila', 'si', 'Sen.', 'Cynthia', 'Villar', 'ug', 'Sen.', 'Ronald', '“Bato”', 'Dela', 'Rosa', 'karong', 'gabii', ',', 'Mar.', '7', ',', '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, 1, 2, 0, 0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,213
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gi-relieve', 'ug', 'gi-reassign', 'ang', 'tibuok', 'pwersa', 'sa', 'kapulisan', 'sa', 'Bayawan', 'City', 'ngadto', 'sa', 'Camp', 'Francisco', 'Fernandez', 'Jr.', 'sa', 'Agan-an', 'sa', 'Sibulan', 'isip', 'parte', 'sa', 'tactical', 'ug', 'operational', 'strategy', 'aron', 'mapulihan', 'og', 'bag-ong', 'mga', 'pulis', 'samtang', 'nagpadayon', 'ang', 'manhunt', 'operation', 'sa', 'mga', 'nipatay', 'ni', 'kanhi', 'Gov.', 'Roel', 'Degamo', 'ug', 'lain', 'pang', 'walo', 'ka', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 5, 6, 6, 6, 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, 1, 2, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,214
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagbangga', 'ang', 'Maayo', 'Shipping', 'Roro', 'ug', 'Petro', 'Helen', 'Tanker', 'sa', 'Tampi', 'Port', ',', 'San', 'Jose', 'sa', 'Negros', 'Oriental', 'mga', '9:30', 'sa', 'buntag', 'karong', 'adlawa', ',', 'March', '7', ',', '2023.', 'Sa', 'pagkakaron', ',', 'dili', 'pa', 'klaro', 'ang', 'hinungdan', 'sa', 'insidente', 'apan', 'wala'y', 'natala', 'nga', 'naangol', 'kon', 'oil', 'spill', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 7, 8, 8, 0, 7, 8, 8, 0, 5, 6, 6, 6, 6, 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]
|
cebuaner
|
4,215
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nibisita', 'ang', 'lima', 'pa', 'ka', 'senador', 'sa', 'haya', 'ni', 'Gov.', 'Roel', 'Degamo', 'sa', 'Junob', ',', 'Dumaguete', 'City', 'karong', 'adlawa', '(', 'Mar.', '7', ',', '2023', ')', '.', 'Sila', 'mao', 'si', 'Senate', 'President', 'Juan', 'Miguel', 'Zubiri', ',', 'Sen.', 'JV', 'Ejercito', ',', 'Sen.', 'Pia', 'Cayetano', ',', 'Sen.', 'Sherwin', 'Gatchalian', ',', 'ug', 'Sen.', 'Joel', 'Villanueva', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 1, 2, 0]
|
cebuaner
|
4,216
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGROS', 'ISLAND', 'REGION', ',', 'GIAPRUBAHAN', 'NA', 'SA', 'KAMARA', 'Giaprobahan', 'sa', 'House', 'of', 'Representatives', 'niadtong', 'Lunes', 'ang', 'ikatulo', 'ug', 'katapusan', 'nga', 'pagbasa', 'sa', 'usa', 'ka', 'balaudnon', 'nga', 'magtukod', 'sa', 'Negros', 'Island', 'Region', '(', 'NIR', ')', '.', 'Nakakuha', 'og', '290', 'ka', 'yes', 'votes', 'ang', 'House', 'Bill', '7355', ',', 'nga', 'maggrupo', 'sa', 'mga', 'syudad', ',', 'munisipalidad', 'ug', 'barangay', 'nga', 'nahimutang', 'sa', '10', 'ka', 'mga', 'probinsya', 'sa', 'Negros', 'Oriental', ',', 'Negros', 'Occidental', ',', 'ug', 'Siquijor', ',', 'lakip', 'na', 'ang', 'Bacolod', 'City', ',', 'ngadto', 'sa', 'Negros', 'Island', 'Region.', 'Anaa', 'sab', 'Technical', 'Working', 'Group', '(', 'TWG', ')', 'nga', 'magtukod', 'sa', 'bag-ong', 'rehiyon', 'nga', 'maglakip', 'sa', 'mga', 'representante', 'gikan', 'sa', 'Office', 'of', 'the', 'President', ',', 'Department', 'of', 'Budget', 'and', 'Management', ',', 'Department', 'of', 'the', 'Interior', 'and', 'Local', 'Government', ',', 'mga', 'representante', 'sa', 'Office', 'of', 'the', 'Governor', 'sa', 'mga', 'probinsya', 'sa', 'Negros', 'Oriental', ',', 'Negros', 'Occidental', 'ug', 'Siquijor', ',', 'ug', 'tanang', 'napili', 'nga', 'District', 'Representatives', 'sa', 'naasoy', 'nga', 'mga', 'probinsya', ',', 'apil', 'na', 'ang', 'Representative', 'of', 'Bacolod', 'City', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 6, 0, 0, 0, 0, 3, 0, 0, 3, 4, 4, 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 5, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 6, 6, 0, 0, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 0, 3, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 5, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0]
|
cebuaner
|
4,217
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Upat', 'ka', 'senador', 'ang', 'nibisita', 'sa', 'haya', 'ni', 'Governor', 'Roel', 'Degamo', 'sa', 'Junob', ',', 'Dumaguete', 'City', 'karong', 'gabii', ',', 'Mar.', '6', ',', '2023.', 'Sila', 'mao', 'si', 'Sen.', 'Bong', 'Revilla', ',', 'Sen.', 'Robin', 'Padilla', ',', 'Sen.', 'Bong', 'Go', ',', 'ug', 'Sen.', 'Jinggoy', 'Estrada', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 1, 2, 0, 0, 0, 1, 2, 0]
|
cebuaner
|
4,218
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGOR', '3RD', 'DISTRICT', 'REP.', 'ARNIE', 'TEVES', ',', 'GIKLARO', 'NGA', 'WALA', 'SIYA'Y', 'KALAMBIGITAN', 'SA', 'KAMATAYON', 'NI', 'KANHI', 'GOV.', 'DEGAMO', 'Nipagawas', 'og', 'video', 'si', 'Negros', 'Oriental', '3rd', 'District', 'Rep.', 'Arnolfo', 'Teves', 'alang', 'sa', 'mga', 'tawo', 'nga', 'giingong', 'ganahan', 'siyang', 'ilambigit', 'sa', 'kamatayon', 'ni', 'kanhi', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'ug', 'gisulbi', 'nga', 'wala', 'siya'y', 'makuha', 'sa', 'pagpatay.', 'Sa', 'usa', 'ka', 'Facebook', 'video', 'message', ',', 'gibutyag', 'ni', 'Teves', 'nga', 'mao', 'kini', 'ang', 'iyang', 'gikahadlukang', 'mahitabo', 'tungod', 'gilaoman', 'niya', 'nga', 'siya', 'ang', 'pasanginlan.', 'Mao', 'kini', 'ang', 'gibutyag', 'ni', 'Teves', 'human', 'niya', 'mahibal-an', 'nga', 'mahimo', 'siyang', 'ilambigit', 'sa', 'maong', 'insidente', 'sa', 'usa', 'ka', 'partido', 'nga', 'wala', 'niya', 'nganli', 'Gisubli', 'sab', 'ni', 'Teves', 'nga', 'wala', 'siya'y', 'makuha', 'ug', 'iyang', 'igsuon', 'sa', 'kamatayon', 'ni', 'Degamo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 5, 6, 6, 6, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 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, 1, 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, 1, 0]
|
cebuaner
|
4,219
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HAYA', 'NI', 'KANHI', 'GOV.', 'DEGAMO', ',', 'IBALHIN', 'SA', 'PROVINCIAL', 'CAPITOL', 'KARONG', 'MAR.', '8', 'Ibalhin', 'ang', 'haya', 'ni', 'kanhi', 'Governor', 'Roel', 'Degamo', 'sa', 'Provincial', 'Capitol', 'karong', 'Miyerkules', ',', 'Marso', '8', ',', 'ug', 'ihaya', 'kini', 'hangtod', 'sa', 'Biyernes', ',', 'Marso', '10', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 1, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,220
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'SUSPEK', 'SA', 'PAGPATAY', 'NI', 'DEGAMO', ',', 'GIPASAKAAN', 'NA', 'OG', 'MGA', 'KASO', 'SA', 'KAPULISAN', 'Gipasaka', 'na', 'sa', 'kapulisan', 'ang', 'daghang', 'mga', 'kaso', 'batok', 'sa', 'mga', 'suspek', 'sa', 'likod', 'sa', 'armadong', 'pag-atake', 'ug', 'pagpatay', 'ni', 'Negros', 'Oriental', 'Governor', 'Roel', 'Degamo', 'niadtong', 'Sabado', ',', 'Marso', '4', ',', '2023.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'special', 'group', 'nga', 'gitahasang', 'mosusi', 'sa', 'maong', 'kaso', 'karong', 'adlawa', ',', 'Marso', '6', ',', '2023.', 'Sumala', 'pa', 'sa', 'tigpamaba', 'sa', 'Special', 'Investigation', 'Task', 'Group', '(', 'SITG', ')', 'Degamo', 'nga', 'si', 'P', '/', 'Lt.', 'Si', 'Col.', 'Gerard', 'Pelare', ',', 'nag-atubang', 'ang', 'mga', 'giingong', 'gunmen', 'og', 'multiple', 'murder', ',', 'ug', 'illegal', 'possession', 'of', 'firearms', 'and', 'explosives.', 'Gisang-at', 'ang', 'unang', 'duha', 'ka', 'kaso', 'sa', 'Provincial', 'Prosecutor’s', 'Office', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'ganihang', 'buntag', ',', 'samtang', 'ang', 'laing', 'duha', 'gipasaka', 'sa', 'Bayawan', 'City', 'Prosecutor’s', 'Office', 'sayo', 'sa', 'kadlawon', 'sa', 'susamang', 'adlaw.', 'Sa', 'pagsulat', 'niining', 'balita', ',', 'anaa', 'sa', 'kustodiya', 'sa', 'kapulisan', 'ang', 'upat', 'ka', 'mga', 'suspetsadong', 'gunmen', ',', 'kinsa', 'nasikop', 'sa', 'Bayawan', 'City', 'pila', 'ka', 'oras', 'ang', 'nilabay', 'human', 'sa', 'pagpatay', 'ni', 'Degamo.', 'Laing', 'suspek', 'sab', 'ang', 'napatay', 'sa', 'gikatahong', 'pinusilay', 'sa', 'mga', 'kapulisan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 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, 5, 6, 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, 3, 4, 4, 4, 4, 4, 4, 4, 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, 3, 4, 4, 0, 0, 0, 5, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,221
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Na-recover', 'sa', 'mga', 'awtoridad', 'ganinang', 'kaadlawon', '(', 'Mar.', '5', ',', '2023', ')', 'ang', 'mga', 'taas', 'nga', 'armas', 'nga', 'giingong', 'gigamit', 'sa', 'pagpusil', 'patay', 'kang', 'Gov.', 'Roel', 'Degamo', 'ug', '9', 'uban', 'pa.', 'Lakip', 'sa', 'mga', 'nakuhang', 'armas', 'mao', 'ang', 'lima', 'ka', 'assault', 'rifle', ',', 'usa', 'ka', 'B40', '(', 'RPG', ')', 'nga', 'dunay', '5', 'ka', 'bala', ';', '4', 'ka', 'bandoliers', ';', 'usa', 'ka', 'rifle', 'case', ';', '2', 'ka', 'combat', 'uniform', ',', 'usa', 'ka', 'grey', 'sweatshirt', ';', 'ug', '3', 'ka', 'pares', 'sa', 'combat', 'boots', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 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]
|
cebuaner
|
4,222
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Duna', 'nay', 'bag-ong', 'gobernador', 'ang', 'Negros', 'Oriental.', 'Kini', 'human', 'nanumpa', 'si', 'Vice', 'Governor', 'Carlo', 'Jorge', 'Joan', '“Guido”', 'Reyes', 'isip', 'gobernador', 'sa', 'lalawigan', 'karong', 'gabii', '(', 'Mar.', '4', ',', '2023', ')', 'human', 'gipusil', 'patay', 'si', 'Gov.', 'Roel', 'Degamo.', 'Nanumpa', 'siya', 'atubangan', 'ni', 'DILG', 'Secretary', 'Benjamin', 'Abalos', 'Jr', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 3, 0, 1, 2, 2, 0]
|
cebuaner
|
4,223
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikondenar', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ang', 'pagpatay', 'ang', 'iyang', 'kaalyado', 'nga', 'si', 'Gov.', 'Roel', 'Degamo.', 'Sa', 'usa', 'ka', 'Facebook', 'post', ',', 'nipasalig', 'si', 'Marcos', 'nga', 'dili', 'mohunong', 'ang', 'iyang', 'administrasyon', 'hangtod', 'nga', 'mahatagan', 'og', 'hustisya', 'ang', 'pagkamatay', 'sa', 'gobernador', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 7, 8, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,224
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nitaliwan', 'na', 'si', 'Gov.', 'Roel', 'Degamo', 'human', 'siya', 'gipusil', 'sulod', 'sa', 'iyang', 'panimalay', 'sa', 'Barangay', 'Nuebe', 'sa', 'lungsod', 'sa', 'Pamplona', 'ganinang', 'buntag', ',', 'Mar.', '4', ',', '2023.', 'Kini', 'gikumpirmar', 'sa', 'kaalyado', 'ni', 'Degamo', 'nga', 'si', 'Siaton', 'Mayor', 'Fritz', 'Diaz', 'ngadto', 'sa', 'local', 'media', '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, 1, 2, 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, 1, 0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,225
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giimbestigaran', 'karon', 'sa', 'kapulisan', 'ang', 'pagpusil', 'kang', 'Gov.', 'Roel', 'Degamo', 'sulod', 'sa', 'compound', 'niini', 'sa', 'Barangay', 'Nuebe', 'sa', 'lungsod', 'sa', 'Pamplona', 'karong', 'buntag', ',', 'Mar.', '4', ',', '2023.', 'Daling', 'gidala', 'sa', 'tambalanan', 'si', 'Degamo.', 'Sa', 'pagkakaron', ',', 'wala', 'pay', 'mahatag', 'nga', 'dugang', 'impormasyon', 'ang', 'mga', 'awtoridad', 'sa', 'naasoy', 'nga', 'lungsod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 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]
|
cebuaner
|
4,226
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BATA', 'NGA', 'NAGDULA', 'RA', 'OG', 'TAGUANAY', ',', 'NAABOT', 'SA', 'LAING', 'NASUD', 'HUMAN', 'NAGTAGO', 'SA', 'CONTAINER', 'VAN', 'Nakauli', 'na', 'sa', 'iyang', 'pamilya', 'ang', 'usa', 'ka', '15-anyos', 'nga', 'batang', 'lalaki', 'sa', 'Bangladesh', 'nga', 'nakaabot', 'sa', 'Malaysia', 'human', 'siya', 'nitago', 'sa', 'usa', 'ka', 'container', 'samtang', 'nakigdula', 'sa', 'iyang', 'higala', 'og', 'taguanay.', 'Sa', 'Twitter', 'post', 'ni', 'Malaysian', 'Interior', 'Minister', 'Saifuddin', 'Nasution', 'niadtong', 'Feb.', '23', ',', 'iyang', 'gianunsyo', 'ang', 'panagkita', 'sa', 'batang', 'si', '"', 'Fahim', '"', 'ug', 'iyang', 'pamilya', 'nidtong', 'Feb.', '21.', 'Sumala', 'pa', 'ni', 'Nasution', ',', 'wala'y', 'nahitabong', '"', 'foul', 'play', '"', 'sa', 'pagkakakandado', 'ni', 'Fahim', 'sulod', 'sa', 'container', 'tungod', 'klaro', 'sab', 'sa', 'istorya', 'sa', 'bata', 'nga', 'nisulod', 'siya', 'sa', 'container', 'aron', 'motago', 'sa', 'iyang', 'higala.', 'Nasayran', 'nga', 'nitago', 'si', 'Fahim', 'sulod', 'sa', 'usa', 'ka', 'container', 'ug', 'nakatulog', 'hangtod', 'aksidenteng', 'na-lock', 'siya', 'sulod', 'niini', 'niadtong', 'Jan.', '11.', 'Wala', 'daw', 'siya', 'kabantay', 'nga', 'nagbiyahe', 'na', 'ang', 'barko', 'nga', 'nahimutangan', 'sa', 'gitaguan', 'niyang', 'container', ',', 'hangtod', 'nga', 'nakaabot', 'kini', 'sa', 'Port', 'Klang', 'sa', 'Malaysia', 'niadtong', 'Jan.', '17', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 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, 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, 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, 5, 6, 0, 5, 0, 0, 0, 0]
|
cebuaner
|
4,227
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TUBIG', 'SA', 'SAN', 'CARLOS', ',', 'NEGROS', 'OCC.', ',', 'NAGPOSITIBO', 'SA', 'COLIFORM', 'Nagpositibo', 'sa', 'coliform', 'ang', 'tubig', 'sa', 'San', 'Carlos', 'City', 'sa', 'Negros', 'Occidental.', 'Mao', 'kini', 'ang', 'pasiunang', 'resulta', 'sa', 'testing', 'sa', 'team', 'sa', 'Department', 'of', 'Health', '(', 'DOH', ')', 'nga', 'niadto', 'didto', 'aron', 'susihon', 'nganong', 'daghan', 'ang', 'nagka-gastroenteritis', 'ug', 'amoebiasis', 'sa', 'lungsod.', 'Nagdeklarar', 'og', 'state', 'of', 'health', 'emergency', 'ang', 'San', 'Carlos', 'City', 'human', 'gatusan', 'ka', 'mga', 'residente', 'didto', 'ang', 'na-ospital', 'tungod', 'sa', 'sakit', 'nga', 'amoebiasis.', 'Duha', 'sab', 'ka', '3-anyos', 'nga', 'bata', 'ang', 'kompirmadong', 'namatay', 'tungod', 'sa', 'maong', 'sakit', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 5, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 5, 6, 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, 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]
|
cebuaner
|
4,228
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikilig', 'ang', 'mga', 'netizen', 'sa', 'dihang', 'nasayran', 'nila', 'nga', 'dunay', 'mag-uyab', 'nga', 'parehong', 'top', 'passer', 'sa', 'bag-o', 'lang', 'nahuman', 'nga', 'Mechanical', 'Engineer', 'Licensure', 'Examination', 'niadtong', 'niaging', 'Pebrero.', 'No.', '1', 'passer', 'si', 'Reynell', 'Villanueva', 'Sanchez', 'human', 'siya', 'nakakuha', 'sa', 'rating', 'nga', '94.9', 'percent', ',', 'samtang', 'ang', 'iyang', 'girlfriend', 'nga', 'si', 'Eunice', 'Santiago', ',', 'No.', '6', 'passer', 'sa', 'naasoy', 'nga', 'pasulit', 'human', 'makuha', 'ang', 'score', 'nga', '93.25', 'percent.', 'Gawas', 'nga', 'pareho', 'silang', 'top', 'passer', ',', 'pareho', 'sab', 'nigraduwar', 'sila', 'si', 'Reynell', 'ug', 'Eunice', 'isip', 'cum', 'laude', 'sa', 'Rizal', 'Technological', 'University', 'didto', 'sa', 'kaulohan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 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, 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, 0, 0, 0, 7, 8, 0, 3, 4, 4, 0, 0, 0, 0]
|
cebuaner
|
4,229
|
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', 'P199', 'NGA', 'PROMO', 'FARES', 'Nagtanyag', 'ang', 'Cebu', 'Pacific', 'og', 'P199', 'nga', 'promotional', 'one-way', 'base', 'fares', 'subay', 'sa', 'pagsaulog', 'sa', 'ilang', 'ika-27', 'nga', 'anibersaryo.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'maong', 'airline', 'niadtong', 'Miyerkules', ',', 'March', '1', ',', '2023.', 'Sa', 'usa', 'ka', 'advisory', ',', 'mahimong', 'mokuha', 'sa', 'maong', 'promotional', 'fares', 'gikan', 'March', '1', 'hangtod', '31', ',', '2023', ',', 'nga', 'naglangkob', 'sa', 'mga', 'domestic', 'flights', 'sa', 'susamang', 'panahon.', 'Apan', ',', 'wala', 'naglakip', 'sa', 'promo', 'ang', 'surcharges', 'ug', 'fees', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,230
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KA', 'BATA', 'PATAY', ',', '469', 'UBAN', 'PA', 'NAIGO', 'SA', 'AMOEBIASIS', 'SA', 'SAN', 'CARLOS', 'CITY', ',', 'NEGROS', 'OCC.', 'Nagdeklarar', 'og', 'state', 'of', 'health', 'emergency', 'ang', 'dakbayan', 'sa', 'San', 'Carlos', 'sa', 'Negros', 'Occidental', 'human', 'naospital', 'ang', 'gatusan', 'ka', 'lumolupyo', 'didto', 'tungod', 'sa', 'sakit', 'nga', 'amoebiasis.', 'Gideklarar', 'kini', 'sa', 'mayor', 'sa', 'maong', 'dakbayan', 'human', 'usa', 'ka', 'ospital', 'didto', 'ang', 'nagtambal', 'sa', '469', 'ka', 'mga', 'kaso', 'sa', 'gidudahang', 'amoebiasis', 'sa', '18', 'ka', 'barangay', 'gikan', 'Pebrero', '1', 'hangtod', 'Marso', '1', ',', '2023.', 'Kini', 'maoy', 'gibutyag', 'ni', 'Joe', 'Alingasa', ',', 'tigpamaba', 'sa', 'incident', 'management', 'team', 'sa', 'San', 'Carlos', 'City', ',', 'Negros', 'Occidental.', 'Sa', 'maong', 'mga', 'kaso', ',', '60', 'porsiyento', 'ang', 'kompirmadong', 'amoebiasis', 'samtang', 'ang', 'uban', ',', 'mga', 'kaso', 'sa', 'acute', 'gastroenteritis.', 'Duha', 'ka', '3', 'anyos', 'nga', 'bata', 'sab', 'ang', 'namatay', 'tungod', 'sa', 'amoebiasis.', 'Gisubli', 'ni', 'Alingasa', 'nga', 'nasubay', 'na', 'sa', 'mga', 'local', 'health', 'authorities', 'ang', 'impeksyon', 'sa', 'mga', 'kontaminadong', 'tinubdan', 'sa', 'tubig', 'ingon', 'man', 'sa', 'gibaligyang', 'streetfood', 'sa', 'usa', 'ka', 'tunghaan', 'sa', 'Barangay', 'Buluangan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 1, 2, 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, 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, 5, 6, 0]
|
cebuaner
|
4,231
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gitay-og', 'na', 'sab', 'og', 'laing', 'magnitude', '4.8', 'nga', 'linog', 'ang', 'habagatang', 'bahin', 'sa', 'Negros', 'Oriental', 'karong', 'gabii', ',', 'Mar.', '2', ',', '2023.', 'Sumala', 'pa', 'sa', 'Phivolcs', ',', 'nasuta', 'ang', 'epicenter', 'kon', 'tinubdan', 'sa', 'maong', 'linog', 'sa', 'kadagatan', 'duol', 'sa', 'lungsod', 'sa', 'Basay', ',', 'Negros', 'Oriental.', 'Nitay-og', 'kini', 'ganinang', '10:32', 'p.m.', 'Nabati', 'ang', 'Intensity', 'III', 'nga', 'pagtay-og', 'sa', 'mga', 'lungsod', 'sa', 'Basay', ',', 'Bayawan', 'City', ',', 'Siaton', ',', 'ug', 'Zamboanguita.', 'Samtang', 'natala', 'sab', 'ang', 'Intensity', 'II', 'nga', 'pagtay-og', 'sa', 'mga', 'lungsod', 'sa', 'Dauin', 'ug', 'Santa', 'Catalina.', 'Hinuon', ',', 'walay', 'aftershocks', 'o', 'kadaot', 'ang', 'gilaomang', 'mataho', 'human', 'sa', 'maong', 'linog.', 'Mahinumduman', 'nga', 'gitay-og', 'sab', 'sa', 'mga', 'nagsagunson', 'nga', 'linog', 'ang', 'habagatang', 'bahin', 'sa', 'Negros', 'Oriental', 'niadtong', 'Miyerkoles', 'sa', 'gabii', ',', 'Mar.', '1', ',', '2023', ',', 'ug', 'kini', 'nilungtad', 'hangtod', 'sa', 'kaadlawon', 'sa', 'Huwebes', ',', 'Mar.', '2', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 3, 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, 0, 5, 0, 5, 6, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,232
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['DUMAGUETE', ',', 'VALENCIA', ',', 'UG', 'DAUIN', ',', 'GINGANLAN', 'NGA', 'TOP', '3', 'NGA', 'PABORITONG', 'ADTUON', 'SA', 'MGA', 'TURISTA', 'SA', 'NEGROS', 'ORIENTAL', 'Usa', 'ang', 'lungsod', 'sa', 'Valencia', 'sa', 'top', '3', 'ka', 'mga', 'paborito', 'nga', 'travel', 'destinations', 'sa', 'Negros', 'Oriental.', 'Nalakip', 'sab', 'sa', 'top', '3', 'nga', 'ranking', 'sa', 'Negros', 'Oriental', 'ang', 'dakbayan', 'sa', 'Dumaguete', 'ug', 'lungsod', 'sa', 'Dauin.', 'Gibase', 'kini', 'sa', 'gidaghanon', 'sa', 'overnight', 'guest', 'arrival', 'report', 'niadtong', '2022', 'nga', 'gipagawas', 'sa', 'Department', 'of', 'Tourism', '(', 'DOT', ')', 'sa', 'Central', 'Visayas.', 'Sa', 'susamang', 'report', ',', 'gipakita', 'nga', 'sa', '76', 'ka', 'mga', 'lungsod', 'ug', 'dakbayan', 'sa', 'Central', 'Visayas', ',', 'anaa', 'sa', 'ika-21', 'nga', 'spot', 'ang', 'Valencia', 'sa', 'most', 'number', 'of', 'overnight', 'guest', 'arrivals', 'niadtong', '2022.', 'Gipakita', 'sab', 'sa', 'maong', 'report', 'nga', 'nakatala', 'ang', 'Negros', 'Oriental', 'og', '194,184', 'nga', 'domestic', 'guests', 'ug', '9,980', 'nga', 'foreign', 'guests', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,233
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['TAIWAN', ',', 'MAGHATAG', 'OG', 'P9,000', 'ALLOWANCE', 'ALANG', 'SA', 'MGA', 'TURISTA', 'NGA', 'MOBISITA', 'SA', 'NASUD', 'Giaprobahan', 'sa', 'gobyerno', 'sa', 'Taiwan', 'niadtong', 'Feb.', '23', 'ang', 'gisugyot', 'sa', 'tourism', 'bureau', 'niini', 'nga', 'paghatag', 'og', 'allowance', 'nga', 'NT', '$', '5,000', 'matag', 'usa', '(', 'P9,000', ')', 'sa', '500,000', 'ka', 'international', 'tourists', 'nga', 'mobiyahe', 'sa', 'nasud.', 'Samtang', ',', 'matag', 'grupo', 'nga', 'aduna'y', '8', 'ngadto', 'sa', '14', 'ka', 'miyembro', 'makadawat', 'og', 'pocket', 'money', 'nga', 'NT', '$', '10,000', '(', 'P17,980', ')', 'ug', 'kadtong', 'mas', 'daghan', 'pa', 'og', 'miyembro', 'makadawat', 'og', 'NT', '$', '20,000', '(', 'P35,960', ')', '.', 'Gipanan-aw', 'nga', 'mag-apod-apod', 'og', 'salapi', 'ang', 'bureau', 'ngadto', 'sa', '90,000', 'ka', 'mga', 'grupo', ',', 'sumala', 'pa', 'sa', 'report', 'sa', 'Taipei', 'Times.', 'Parte', 'ang', 'maong', 'lakang', 'sa', 'NT', '$', '5.3', 'billion', '(', 'P9.5', 'billion', ')', 'nga', 'proyekto', 'sa', 'gobyerno', 'sa', 'Taiwan', 'aron', 'makakuha', 'og', '6', 'milyon', 'ka', 'mga', 'turista', 'karong', 'tuiga', 'ug', '10', 'milyon', 'sa', '2025.', 'Sumala', 'pa', 'ni', 'Tourism', 'Bureau', 'Director-General', 'Chang', 'Shi-chung', ',', 'kung', 'usa', 'ka', 'sa', 'mga', 'turista', 'nga', 'mahatagan', 'sa', 'NT', '$', '5,000', 'nga', 'incentive', ',', 'madawat', 'kini', 'nimo', 'nga', 'kantidad', 'pag-abot', 'sa', 'Taiwan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 3, 4, 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, 3, 4, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]
|
cebuaner
|
4,234
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PILGRIM', 'RELICS', 'NI', 'ST.', 'THERESE', ',', 'MOBISITA', 'PAG-USAB', 'SA', 'NEGROS', 'ORIENTAL', 'Mobisita', 'pag-usab', 'sa', 'Diocese', 'sa', 'Dumaguete', 'ang', 'Pilgrim', 'Relics', 'ni', 'St.', 'Therese', 'of', 'the', 'Child', 'Jesus', 'karong', 'Lunes', ',', 'March', '6', ',', '2023.', 'Parte', 'kini', 'sa', 'ikalima', 'nga', 'pagbisita', 'sa', 'Pilgrim', 'Relics', 'ni', 'St.', 'Therese', 'sa', 'Pilipinas', ',', 'uban', 'sa', 'tema', 'nga', '"', 'Lakbay', 'tayo', ',', 'St.', 'Therese', '!', 'Ka-alagad', ',', 'Kaibigan', ',', 'Ka-Misyon', '!', '"'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 1, 0, 0, 0, 0, 5, 6, 0, 0, 0, 5, 0, 5, 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, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,235
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HABAGATANG', 'BAHIN', 'SA', 'NEGROS', 'ORIENTAL', 'GITAY-OG', 'SA', 'NAGSAGUNSON', 'NGA', 'LINOG', 'Gitay-og', 'sa', 'serye', 'sa', 'mga', 'linog', 'ang', 'pipila', 'ka', 'bahin', 'sa', 'Negros', 'Oriental', ',', 'sumala', 'pa', 'sa', 'Phivolcs.', 'Ang', 'epicenter', 'kon', 'tinubdan', 'sa', 'maong', 'mga', 'linog', 'nasuta', 'sa', 'kadagatan', 'duol', 'sa', 'lungsod', 'sa', 'Basay.', 'Unang', 'natala', 'ang', 'magnitude', '4.2', 'nga', 'linog', 'pasado', 'alas-11', 'kagabii', '(', 'Mar.', '1', ',', '2023', ')', '.', 'Nabati', 'kini', 'sa', 'lungsod', 'sa', 'Sibulan', ',', 'ingon', 'man', 'sa', 'Dapitan', 'City', ',', 'Zamboanga', 'del', 'Norte', ',', 'ug', 'sa', 'Argao', 'sa', 'probinsya', 'sa', 'Sugbo.', 'Gisundan', 'kini', 'sa', 'mga', 'linog', 'nga', 'dunay', 'mga', 'magnitude', 'gikan', 'sa', '1.5', 'ngadto', 'sa', '3.1', 'nga', 'nilungtad', 'hangtod', 'alas-3', 'ganinang', 'kaadlawon', ',', 'Mar.', '2', ',', '2023.', 'Hinuon', ',', 'walay', 'gilaumang', 'kadaot', 'o', 'aftershocks', 'human', 'sa', 'maong', 'mga', 'linog', ',', 'dugang', 'pa', 'sa', 'Phivolcs', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 3, 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, 5, 0, 0, 0, 0, 5, 6, 0, 5, 6, 6, 0, 0, 0, 5, 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, 3, 0]
|
cebuaner
|
4,236
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['AMLAN', 'ZOO', ',', 'MAKADAWAT', 'OG', 'HINABANG', 'GIKAN', 'SA', 'PROVINCIAL', 'GOV'T', 'ALANG', 'SA', 'KAHIMSOG', 'SA', 'MGA', 'HAYOP', 'NIINI', 'Nihangyo', 'ang', 'presidente', 'sa', 'Dreamland', 'Nature', 'and', 'Adventure', 'Park', '(', 'DNAP', ')', 'nga', 'si', 'Wilfredo', 'Chiu', 'uban', 'ni', 'kanhi', 'Amlan', 'Mayor', 'Bentham', 'dela', 'Cruz', 'ngadto', 'ni', 'Governor', 'Roel', 'Degamo', 'alang', 'sa', 'dinalian', 'nga', 'interbensyon', 'aron', 'mabalik', 'ang', 'kahimsog', 'sa', 'mga', 'hayop', 'ug', 'pagpa-ayo', 'sab', 'sa', 'pasilidad.', 'Nag-operate', 'ang', 'DNAP', 'sukad', 'pa', 'niadtong', '2015', 'ilalom', 'sa', 'usa', 'ka', 'Public-Private', 'Partnership', '(', 'PPP', ')', 'uban', 'sa', 'LGU-Amlan.', 'Nag-atubang', 'kini', 'og', 'labing', 'kalisod', 'sa', 'mga', 'niaging', 'tuig', 'tungod', 'sa', 'pandemya', 'sa', 'Covid-19', 'ug', 'kakulang', 'sa', 'suporta', 'gikan', 'sa', 'LGU.', 'Daling', 'gimandoan', 'ni', 'Degamo', 'ang', 'Provincial', 'Veterinarian', ''s', 'Office', '(', 'PVO', ')', 'ug', 'Environment', 'and', 'Natural', 'Resources', 'Division', '(', 'ENRD', ')', 'aron', 'paghatag', 'sa', 'gikinahanglan', 'nga', 'tabang', 'sa', 'maong', 'parke', ',', 'ilabi', 'na', 'alang', 'sa', 'kahimsog', 'sa', 'mga', 'hayop', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 2, 0, 0, 0, 5, 0, 1, 2, 2, 0, 0, 0, 1, 2, 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, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,237
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ULO', 'SA', 'GI-CHOP-CHOP', 'NGA', 'MODEL', 'SA', 'HONG', 'KONG', ',', 'NAKIT-AN', 'NA', 'Nakit-an', 'na', 'ang', 'ulo', 'sa', 'gipatay', 'nga', 'modelo', 'sa', 'Hong', 'Kong', 'nga', 'anaa', 'sa', 'sulod', 'sa', 'kaldero', ',', 'samtang', 'ang', 'iyang', 'batiis', 'anaa', 'sa', 'sulod', 'sa', 'refrigerator', 'sa', 'usa', 'ka', 'apartment', 'sa', 'maong', 'nasud.', 'Narekober', 'sab', 'sa', 'crime', 'scene', 'ang', 'martilyo', ',', 'chain', 'saw', ',', 'ug', 'makina', 'nga', 'gilingan', 'sa', 'karne.', 'Gikasuhan', 'na', 'og', 'murder', 'ang', 'kanhi', 'bana', 'ni', 'Abby', 'Choi', 'nga', 'si', 'Alex', 'Kwong', 'lakip', 'na', 'ang', 'amahan', ',', 'inahan', ',', 'ug', 'igsuon', 'niini', 'tungod', 'sa', 'obstruction', 'of', 'justice.', 'Giingong', 'motibo', 'sa', 'krimen', 'mao', 'ang', 'panag-away', 'tungod', 'sa', 'mga', 'kabtangan.', 'Nagsugod', 'kuno', 'ang', 'panagbangi', 'sa', 'dihang', 'gisulayan', 'og', 'baligya', 'sa', 'biktima', 'ang', 'gipalit', 'niya', 'nga', 'balay', 'ug', 'yuta', 'nga', 'nakapangalan', 'sa', 'ugangan', 'niyang', 'lalaki.', 'Duda', 'sab', 'sa', 'mga', 'awtoridad', 'nga', 'giplanohan', 'ang', 'krimen', 'ug', 'gikuha', 'ang', 'maong', 'apartment', 'aron', 'gamiton', 'sa', 'pagdispatsa', 'sa', 'patay', 'nga', 'lawas', 'sa', 'biktima.', 'Dugang', 'pa', 'nila', ',', 'mahimong', 'gipatay', 'una', 'si', 'Choi', 'sulod', 'sa', 'van', 'sa', 'wala', 'pa', 'gidala', 'sa', 'apartment', 'ug', 'didto', 'na', 'gi-chop-chop.', 'Sa', 'pagkakaron', ',', 'padayon', 'pang', 'gibulong', 'sa', 'kapulisan', 'didto', 'ang', 'ubang', 'parte', 'sa', 'lawas', 'ni', 'Choi', 'lakip', 'na', 'ang', 'iyang', 'kamot', 'ug', 'sa', 'tunga', 'nga', 'bahin', 'sa', 'iyang', 'lawas.', 'Nakatutok', 'ang', 'search', 'operation', 'sa', 'sementeryo', 'diin', 'nakit-ang', 'magkauban', 'si', 'Kwong', 'ug', 'iyang', 'amahan', 'sa', 'wala', 'pa', 'sila', 'masikop', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 5, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 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, 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, 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]
|
cebuaner
|
4,238
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KANHI', 'CAFGU', ',', 'GIPUSIL', 'PATAY', 'SA', 'AYUNGON', 'Usa', 'ka', 'kanhi', 'Civilian', 'Armed', 'Forces', 'Geographical', 'Unit', '(', 'CAFGU', ')', 'ang', 'gipusil', 'patay', 'sa', 'Barangay', 'Amdus', 'sa', 'lungsod', 'sa', 'Ayungon', 'mga', '7:10', 'sa', 'gabii', 'niadtong', 'Feb.', '26', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'nakalas', 'nga', 'biktima', 'nga', 'si', 'Teotimo', 'Sisoy', 'Jr.', ',', '47', 'anyos', ',', 'minyo', ',', 'ug', 'lumolupyo', 'sa', 'Barangay', 'Cabang', ',', 'Jimalalud.', 'Sumala', 'pa', 'sa', 'imbestigasyon', 'sa', 'kapulisan', ',', 'nagbiyahe', 'ang', 'biktima', 'paingon', 'sa', 'Barangay', 'Tibyawan', 'sa', 'maong', 'lungsod', 'sa', 'dihang', 'gipusil', 'kini', 'sa', 'upat', 'ka', 'mga', 'wala', 'pa', 'mailhing', 'suspek', 'kinsa', 'nagsakay', 'og', 'duha', 'ka', 'motorsiklo.', 'Human', 'sa', 'maong', 'insidente', ',', 'dali', 'sab', 'nga', 'nisibat', 'ang', 'mga', 'suspek', 'paingon', 'sa', 'direksyon', 'sa', 'Mabinay.', 'Tungod', 'niini', ',', 'nakaangkon', 'og', 'samad', 'pinusilan', 'sa', 'kalawasan', 'ang', 'biktima.', 'Gidala', 'kini', 'sa', 'Bindoy', 'District', 'Hospital', 'apan', 'gideklarar', 'nga', 'dead', 'on', 'arrival', 'sa', 'nag-atiman', 'nga', 'doktor.', 'Narekober', 'sab', 'sa', 'crime', 'scene', 'ang', 'unom', 'ka', 'empty', 'shells', 'sa', 'caliber', '45.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'bahin', '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, 3, 0, 0, 0, 0, 5, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 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, 0, 0, 0, 1, 2, 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, 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, 5, 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, 0, 0, 0, 0]
|
cebuaner
|
4,239
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['500,000', 'ka', 'mga', 'air', 'tickets', 'ang', 'ihatag', 'sa', 'Cathay', 'Pacific', 'isip', 'suporta', 'sa', '"', 'Hello', 'Hong', 'Kong', '"', 'campaign', 'sa', 'Tourism', 'Board', 'sa', 'Hong', 'Kong', 'aron', 'pag-welcome', 'sa', 'mga', 'turista.', 'Mao', 'kini', 'ang', 'gianunsyo', 'sa', 'maong', 'airline', 'uban', 'sa', 'mga', 'detalye', 'aron', 'makakuha', 'og', 'kahigayonan', 'nga', 'makuha', 'sa', 'usa', 'sa', 'mga', 'ticket.', 'Anaa', 'sa', 'kinatibuk-ang', '80,000', 'ka', 'mga', 'round-trip', 'ticket', 'ang', 'igahin', 'alang', 'sa', 'Southeast', 'Asia', ',', 'diin', '12,500', 'niini', 'ang', 'igahin', 'alang', 'sa', 'mga', 'residente', 'sa', 'Singapore.', 'Aron', 'mahimong', 'kwalipikado', 'sa', 'usa', 'ka', 'ticket', ',', 'kinahanglang', 'Cathay', 'member', 'ka', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 6, 0, 0, 0, 3, 4, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0]
|
cebuaner
|
4,240
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Pipila', 'ka', 'mga', 'tawo', 'ang', 'nagtinabangay', 'aron', 'makahatag', 'og', 'donasyon', 'alang', 'sa', 'mga', 'hayop', 'sa', 'Amlan', 'Zoo.', 'Gipangunahan', 'ang', 'maong', 'kalihokan', 'ni', 'Corrine', 'Utzurrum-Saligue', 'kuyog', 'ang', 'mga', 'personal', 'higala', 'niini.', 'Mahinumduman', 'kaniadto', ',', 'usa', 'ka', 'netizen', 'ang', 'nag-share', 'sa', 'kahimtang', 'sa', 'mga', 'hayop', 'sa', 'maong', 'zoo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 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]
|
cebuaner
|
4,241
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['FEB.', '25', ',', '2023', ',', 'DILI', 'NA', 'HOLIDAY', 'USA', 'NA', 'LANG', 'KA', 'WORKING', 'DAY', '—MALACAÑANG', 'Dili', 'na', 'holiday', 'ang', 'February', '25', 'human', 'kini', 'gibalhin', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'ngadto', 'sa', 'February', '24', ',', 'sumala', 'pa', 'sa', 'Malacañang', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,242
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'amahan', 'ang', 'grabe', 'ang', 'kaguol', 'ug', 'naghilak', 'sa', 'dihang', 'makita', 'niya', 'ang', 'patay', 'nga', 'lawas', 'sa', 'iyang', 'anak', 'nga', 'nakatunob', 'og', 'live', 'wire', 'sa', 'Barangay', 'Acereda', ',', 'Bobon', 'sa', 'Northern', 'Samar', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 6, 6, 6, 0, 5, 6, 0]
|
cebuaner
|
4,243
|
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', 'Biyernes', ',', 'Pebrero', '24', ',', '2023', ',', 'human', 'kini', 'gideklarar', 'ni', 'Presidente', 'Ferdinand', 'Marcos', 'Jr.', 'isip', 'special', 'non-working', 'holiday', 'aging', 'paghandum', 'sa', 'anibersaryo', 'sa', 'EDSA', 'People', 'Power', 'Revolution.', 'Apan', 'giklaro', 'sab', 'sa', 'Malacañang', 'nga', 'working', 'day', 'ang', 'Sabado', ',', 'Pebrero', '25', ',', '2023', ',', 'nga', 'maoy', 'mismong', 'adlaw', 'sa', 'naasoy', 'nga', 'anibersaryo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 0, 7, 8, 8, 8, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,244
|
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', 'hulagway', 'sa', ''colorized', 'photo', ''', 'sa', 'tinuig', 'nga', 'Founder', ''s', 'Day', 'parade', 'sa', 'Central', 'Visayas', 'Polytechnic', 'College', '(', 'CVPC', ')', 'sa', 'Perdices', 'Street', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'niadtong', '1990.', 'Ang', 'CVPC', 'mao', 'na', 'karon', 'ang', 'Negros', 'Oriental', 'State', 'University', '(', 'NORSU', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 7, 8, 8, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 5, 6, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0]
|
cebuaner
|
4,245
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Giawhag', 'ang', 'tanan', 'ilabi', 'na', 'kadtong', 'mga', 'naggamit', 'pa', 'og', 'analog', 'cellphone', 'o', 'dili', 'touch', 'screen', 'nga', 'mobisita', 'sa', 'lobby', 'sa', 'Provincial', 'Convention', 'Center', 'sa', 'Dumaguete', 'City', 'alang', 'sa', 'pagrehistro', 'sa', 'mga', 'sim', 'cards.', 'Mahimong', 'moadto', 'karong', 'adlawa', ',', 'February', '23', ',', '2023', ',', 'gikan', '9:00am', 'hangtod', '2:00pm.', 'Magdala', 'lang', 'og', 'usa', 'ka', 'government-issued', 'ID.', 'Mahuman', 'ang', 'SIM', 'Card', 'Registration', 'sa', 'April', '26', ',', '2023.', 'Human', 'niini', ',', 'automatic', 'nga', 'ma-invalidate', 'o', 'dili', 'na', 'magamit', 'ang', 'mga', 'sim', 'card', 'nga', 'wala', 'marehistro', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 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, 0, 0, 0]
|
cebuaner
|
4,246
|
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', 'Pagasa', 'ang', 'hulagway', 'sa', ''waxing', 'crescent', 'moon', ''', 'uban', 'sa', 'Venus', 'ug', 'Jupiter', 'niadtong', 'Miyerkules', ',', 'Feb.', '22.', 'Gianunsyo', 'sab', 'sa', 'maong', 'state', 'weather', 'bureau', 'nga', 'abri', 'na', 'sa', 'walk-in', 'guests', 'ang', 'Pagasa', 'Astronomical', 'Observatory', 'hangtod', 'Feb.', '24', ',', 'gikan', '7:00pm', 'hangtod', '10:00pm', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,247
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CAMANJAC-MAGATAS', 'SECTION', 'SA', 'METRO', 'DUMAGUETE', 'DIVERSION', 'ROAD', ',', 'MASUMPAYAN', 'NA', 'Gihawanan', 'niadtong', 'Sabado', 'ang', 'bahin', 'sa', 'Camanjac-Magatas', 'sa', 'Dumaguete', 'Diversion', 'Road.', 'Sugdan', 'na', 'ang', 'pagkonkreto', 'sa', 'bahin', 'sa', 'maong', 'dalan', 'aron', 'maabrihan', 'na', 'kini', 'alang', 'sa', 'publiko.', 'Sumala', 'pa', 'ni', 'Mayor', 'Felipe', 'Remollo', 'nga', 'kung', 'mahuman', 'ang', '4-lane', 'Metro', 'Dumaguete', 'Diversion', 'Road', ',', 'magsilbi', 'kini', 'nga', 'alternatibong', 'rota', 'sa', 'nga', 'motorista', 'nga', 'moagi', 'sa', 'Sibulan-Dumaguete-Bacong.', 'Gilaoman', 'nga', 'makapamenos', 'kini', 'sa', 'huot', 'nga', 'trapiko', 'sa', 'national', 'highway', 'ug', 'downtown', 'area', 'sa', '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.
|
[5, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 6, 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, 5, 6, 6, 6, 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]
|
cebuaner
|
4,248
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['3', 'KA', 'BATA', 'GIPANGANAK', 'SA', 'PAREHANG', 'PETSA', 'KADA', '3', 'KA', 'TUIG', 'Usa', 'ka', 'netizen', 'gikan', 'sa', 'Laguna', 'ang', 'nag-share', 'sa', 'Facebook', 'sa', 'iyang', 'talagsaon', 'nga', 'storya', 'bahin', 'sa', 'iyang', 'tulo', 'ka', 'mga', 'anak', 'nga', 'gipanganak', 'sa', 'pareha', 'nga', 'petsa', 'human', 'sa', 'kada', 'tulo', 'ka', 'tuig', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]
|
cebuaner
|
4,249
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['META', ',', 'GIANUNSYO', 'ANG', 'USA', 'KA', '"', 'PAID', 'SUBSCRIPTION', 'SERVICE', '"', 'ALANG', 'SA', 'FACEBOOK', ',', 'INSTAGRAM', 'Gipaila', 'sa', 'Meta', 'CEO', 'nga', 'si', 'Mark', 'Zuckerberg', 'ang', 'Meta', 'Verified', ',', 'usa', 'ka', '"', 'paid', 'subscription', 'service', '"', 'alang', 'sa', 'Facebook', 'ug', 'Instagram', ',', 'nga', 'magtugot', 'sa', 'users', 'nga', 'ma-verify', 'ang', 'ilang', 'accounts.', 'Magsugod', 'ang', 'presyo', 'sa', 'Meta', 'Verified', 'ngadto', 'sa', '$', '11.99', '(', 'gibana-banang', 'P660', ')', 'matag', 'bulan', 'sa', 'web', 'o', '$', '14.99', '(', 'gibana-banang', 'P825', ')', 'kada', 'bulan', 'sa', 'iOS.', 'Ipahigayon', 'una', 'ang', 'maong', 'serbisyo', 'sa', 'Australia', 'ug', 'New', 'Zealand', 'karong', 'semanaha'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 3, 0, 0, 0, 1, 2, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0]
|
cebuaner
|
4,250
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'netizen', 'ang', 'nag-share', 'og', 'video', 'sa', 'social', 'media', 'sa', 'kahimtang', 'sa', 'mga', 'hayop', 'sa', 'Amlan', 'Dreamland', 'Zoo.', 'Sumala', 'pa', 'ni', 'Rhanette', 'Love', 'Jorolan', ',', 'nagkinahanglan', 'ang', 'maong', 'zoo', 'og', 'mga', 'pagkaon', 'alang', 'sa', 'mga', 'hayop.', 'Gisubli', 'niya', ',', 'mahimo', 'sab', 'nga', 'magdonar', 'og', 'freezer', 'aron', 'mabutangan', 'sa', 'mga', 'gidonar', 'nga', 'pagkaon', 'tungod', 'guba', 'na', 'ang', 'freezer', 'sa', 'maong', 'zoo.', 'Sa', 'video', ',', 'makita', 'ang', 'tigre', 'nga', 'si', 'Acron', 'nga', 'dili', 'na', 'kaayo', 'mokaon', 'ug', 'naglugos', 'sa', 'pagginhawa.', 'Dugang', 'pa', 'ni', 'Jorolan', ',', 'mahimong', 'personal', 'nga', 'ihatag', 'sa', 'zoo', 'ang', 'mga', 'donasyon', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 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]
|
cebuaner
|
4,251
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['PULIS', 'UG', 'KAWANI', 'SA', 'BJMP', ',', 'GIPUSIL', 'PATAY', 'SA', 'BAYAWAN', 'CITY', 'SA', 'MISMONG', 'ADLAW', 'SA', 'PISTA', 'Usa', 'ka', 'pulis', 'ug', 'usa', 'ka', 'kawani', 'sa', 'Bureau', 'of', 'Jail', 'Management', 'and', 'Penology', '(', 'BJMP', ')', 'ang', 'gipusil', 'patay', 'sa', 'Bayawan', 'City', 'atol', 'sa', 'mismong', 'adlaw', 'sa', 'pista', 'niini', 'kagabii', '(', 'Feb.', '18', ',', '2023', ')', '.', 'Giila', 'sa', 'kapulisan', 'ang', 'mga', 'nakalas', 'nga', 'biktima', 'nga', 'sila', 'si', 'Patrolman', 'Rhulin', 'Mar', 'Abrasaldo', ',', 'kinsa', 'sakop', 'sa', 'Regional', 'Mobile', 'Force', 'Battalion-7', ',', 'ug', 'si', 'Edwin', 'Laag', ',', 'kawani', 'sa', 'BJMP', 'nga', 'nakadestino', 'sa', 'Bayawan', 'City', 'Jail.', 'Naangol', 'sab', 'sa', 'maong', 'pagpamusil', 'si', 'Winchita', 'Jamin', ',', '56', 'anyos.', 'Matud', 'pa', 'sa', 'report', ',', 'nagsakay', 'og', 'yellow', 'nga', 'motor', 'ang', 'duha', 'ka', 'suspek', 'nga', 'nipusil', 'sa', 'mga', 'biktima', 'dapit', 'sa', 'Peping', 'Gamo', 'Street', 'sa', 'Barangay', 'Tinago', 'sa', 'naasoy', 'nga', 'dakbayan.', 'Nagpadayon', 'pa', 'ang', 'imbestigasyon', 'ug', 'hot', 'pursuit', 'operation', 'sa', 'Bayawan', 'PNP', 'aron', 'masuta', 'ang', 'mga', 'mamumuno', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 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, 1, 2, 2, 0, 0, 0, 0, 3, 4, 4, 4, 0, 0, 0, 1, 2, 0, 0, 0, 3, 0, 0, 0, 5, 6, 6, 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, 5, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,252
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'KA', 'MANGINGISDA', ',', 'NAKADAWAT', 'OG', 'MOTORIZED', 'BOATS', 'ISIP', 'SUPORTA', 'SA', 'ILANG', 'PANGINABUHI', 'Duha', 'ka', '7.5', 'HP', 'nga', 'motorized', 'boats', 'ang', 'gidonar', 'ngadto', 'sa', 'mga', 'mangingisda', 'nga', 'sila', 'si', 'Roberto', 'S.', 'Barimbao', 'ug', 'Benjie', 'De', 'Asis', 'aron', 'pagsuporta', 'sa', 'ilang', 'tagsa-tagsa', 'nga', 'panginabuhian.', 'Gidonar', 'kini', 'pinaagi', 'ni', 'Don', 'Terng', 'R.', 'Uypitching', ',', 'umalabot', 'nga', 'Grand', 'Master', 'of', 'Grand', 'Lodge', 'of', 'the', 'Philippines', ',', 'uban', 'ni', 'Mayor', 'Felipe', 'Remollo.', 'Nitambong', 'sab', 'ang', 'pipila', 'ka', 'mga', 'opisyales', 'sa', 'usa', 'ka', 'turnover', 'rites', 'niadtong', 'Huwebes', ',', 'Feb.', '16', ',', 'sa', 'Pantawan', 'II.', 'Ang', 'maong', 'mga', 'benepisyaryo', ',', 'mga', 'miyembro', 'sa', 'Dumaguete', 'City', 'Bangus', 'Fry', 'Association', 'sa', 'Barangay', 'Banilad', 'ug', 'Roberto', 'S.', 'Barimbao', 'sa', 'Looc', 'Fisherfolks', 'Association.', 'Gipili', 'sila', 'pinaagi', 'sa', 'Office', 'of', 'City', 'Agriculturist', 'ug', 'Sectoral', 'Desk', 'Office', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 5, 6, 0, 1, 2, 2, 0, 3, 4, 4, 0, 0, 0, 0, 3, 4, 4, 4, 0, 3, 4, 4, 0]
|
cebuaner
|
4,253
|
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', '"', 'selfie', '"', 'niini', 'nga', 'gikuha', 'sa', 'Perseverance', 'rover', 'niadtong', 'Jan.', '22', ',', 'diin', 'ilang', 'gi-feature', 'ang', 'hazy', 'beige', 'nga', 'kalangitan', 'sa', 'Mars.', 'Gitahasan', 'ang', 'rover', 'sa', 'pagsusi', 'sa', 'features', 'ug', 'pagkolekta', 'sa', 'samples', 'sa', 'maong', 'planeta.', 'Nagsaulog', 'sab', 'kini', 'sa', 'ikaduhang', 'anibersaryo', 'niini', 'sa', 'kawanangan', 'karong', 'adlawa', ',', 'Feb.', '18', ',', '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, 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, 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
|
4,254
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MAULANON', 'NGA', 'PANAHON', ',', 'MASINATI', 'SA', 'VISAYAS', 'UG', 'MINDANAO', 'TUNGOD', 'SA', 'LPA', 'Makasinati', 'og', 'maulanon', 'nga', 'panahon', 'ang', 'pipila', 'ka', 'mga', 'bahin', 'sa', 'Visayas', 'ug', 'Mindanao', 'tungod', 'sa', 'low', 'pressure', 'area', '(', 'LPA', ')', '.', 'Matod', 'pa', 'sa', 'Philippine', 'Atmospheric', ',', 'Geophysical', 'and', 'Astronomical', 'Services', 'Administration', '(', 'PAGASA', ')', 'karong', 'adlawa', ',', 'Feb.', '17', ',', '2023.', 'Sa', 'pinakabag-ong', 'public', 'weather', 'forecast', ',', 'anaa', 'ang', 'LPA', 'sa', '765', 'km', 'sa', 'east', 'southeast', 'sa', 'Hinatuan', ',', 'Surigao', 'del', 'Sur.', 'Sumala', 'pa', 'sa', 'PAGASA', ',', 'mahimong', 'magdala', 'ang', 'trough', 'sa', 'LPA', 'og', 'madag-umon', 'nga', 'kalangitan', 'uban', 'sa', 'katag-katag', 'nga', 'pag-ulan', 'ug', 'thunderstorms', 'sa', 'Eastern', 'Visayas', ',', 'Northern', 'Mindanao', ',', 'Caraga', ',', 'Davao', 'Region', ',', 'ug', 'Bohol.', 'Posible', 'sab', 'ang', 'pagbaha', 'ug', 'landslides', 'sa', 'maong', 'mga', 'dapit', 'tungod', 'sa', 'hinay', 'ngadto', 'sa', 'kusog', 'nga', 'pag-ulan.', 'Samtang', 'ang', 'ubang', 'bahin', 'sa', 'Mindanao', 'makasinati', 'sab', 'og', 'madag-uban', 'nga', 'kalangitan', 'uban', 'sa', 'patak-patak', 'nga', 'pag-ulan', 'o', 'thunderstorms', 'tungod', 'sa', 'localized', 'thunderstorms.', 'Gibutyag', 'sab', 'ni', 'PAGASA', 'weather', 'forecaster', 'Patrick', 'del', 'Mundo', 'nga', 'ilang', 'gi-monitor', 'karon', 'ang', 'lain', 'pang', 'LPA', 'nga', 'anaa', 'sa', '1,110km', 'sa', 'east', 'sa', 'Eastern', 'Visayas.', 'Dugang', 'pa', 'niya', ',', 'aduna'y', 'gamay', 'nga', 'posibilidad', 'nga', 'mahimong', 'bagyo', 'ang', 'duha', 'ka', 'mga', '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, 5, 0, 5, 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, 3, 4, 4, 4, 4, 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, 5, 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, 0, 0, 5, 6, 0, 5, 6, 0, 5, 0, 5, 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, 5, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,255
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', 'UGMA', 'NA', 'ANG', 'LAST', 'WEEKLY', 'DRAW', '!', 'I-atak', 'na', 'ang', 'inyong', 'tanang', 'entries', 'karong', 'adlawa', 'before', '5pm', 'tungod', 'karong', 'Sabado', ',', 'Feb.', '18', ',', 'mao', 'ang', '8th', 'ug', 'last', 'weekly', 'draw', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0]
|
cebuaner
|
4,256
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['GOOD', 'NEWS', 'ALANG', 'SA', 'MGA', 'PINOY', 'SA', 'GAWAS', '!', 'Gitugutan', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', '(', 'BSP', ')', 'ang', 'paggamit', 'sa', 'mga', 'Pilipino', 'og', 'GCash', 'nga', 'aduna'y', 'international', 'SIM's.', 'Ang', 'beta', 'launch', 'niini', 'magtugot', 'sa', 'unang', '1,000', 'ka', 'mga', 'Pilipino', 'sa', 'Japan', ',', 'Australia', ',', 'ug', 'Italy', 'nga', 'mo-sign', 'up', 'sa', 'GCash', 'bisan', 'wala'y', 'Philippine', 'SIM', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-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, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0]
|
cebuaner
|
4,257
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'netizen', 'ang', 'nag-share', 'sa', 'hulagway', 'sa', 'usa', 'ka', 'cloud', 'o', 'panganod', 'diin', 'mura', 'kini'g', 'nag-inusara', 'sa', 'kalangitan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,258
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', '2', 'DAYS', 'NALANG', '!', 'I-atak', 'na', 'ang', 'inyong', 'tanang', 'entries', 'karong', 'semanaha', 'tungod', 'karong', 'Sabado', ',', 'Feb.', '18', ',', 'mao', 'ang', '8th', 'ug', 'last', 'weekly', 'draw', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '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, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0]
|
cebuaner
|
4,259
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', 'BASIN', 'IKAW', 'NA', 'ANG', 'MOSUNOD', 'NGA', 'MILYONARYO', '!', 'I-atak', 'na', 'ang', 'inyong', 'tanang', 'entries', 'karong', 'semanaha', 'tungod', 'karong', 'Sabado', ',', 'Feb.', '18', ',', 'mao', 'ang', '8th', 'ug', 'last', 'weekly', 'draw', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0]
|
cebuaner
|
4,260
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['I-atak', 'na', 'ang', 'inyong', 'tanang', 'entries', 'karong', 'semanaha', 'tungod', 'karong', 'Sabado', ',', 'Feb.', '18', ',', 'mao', 'ang', '8th', 'ug', 'last', 'weekly', 'draw', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '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, 0, 7, 8, 8, 8, 8, 0, 0]
|
cebuaner
|
4,261
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'na', 'sa', 'dul-an', '40,000', 'ang', 'mga', 'kompirmadong', 'namatay', 'tungod', 'sa', 'kusog', 'nga', 'magnitude', '7.8', 'nga', 'linog', 'nga', 'nitay-og', 'sa', 'Turkey', 'ug', 'Syria', 'niadtong', 'Feb.', '6', ',', '2023.', 'Sumala', 'pa', 'sa', 'pinakabag-ong', 'update', ',', '35,418', 'ang', 'nakalas', 'sa', 'Turkey', 'samtang', '3,688', 'ang', 'nakabsan', 'sa', 'kinabuhi', 'sa', 'Syria.', 'Tungod', 'niini', ',', 'nisaka', 'na', 'ngadto', 'sa', '39,106', 'ang', 'kinatibuk-ang', 'mga', 'namatay', 'sa', 'duha', 'ka', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0]
|
cebuaner
|
4,262
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', 'APIL', 'NA', 'KAY', '3', 'DAYS', 'NA', 'LANG', '!', 'Mao', 'kini', 'ang', 'mga', 'Proof-of-Purchase', '(', 'POP', ')', 'alang', 'sa', 'imong', 'entries', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '2023', '!', 'Join', 'now', 'and', 'get', 'a', 'chance', 'na', 'mahimong', 'milyonaryo', 'sa', 'Grand', 'Draw', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,263
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Karong', 'adlawa', 'mao', 'ang', 'ika-99', 'nga', 'birthday', 'ni', 'kanhi', 'Senate', 'President', 'ug', 'kasamtangang', 'chief', 'presidential', 'legal', 'counsel', 'nga', 'si', 'Juan', 'Ponce', 'Enrile.', 'Sa', 'iyang', 'edad', ',', 'gikonsiderar', 'si', 'Enrile', 'nga', 'mas', 'gulang', 'pa', 'sa', 'World', 'War', 'II', ',', 'penicillin', ',', 'ug', 'sa', 'World', 'Health', 'Organization', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 3, 4, 4, 0]
|
cebuaner
|
4,264
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['ESKWELAHAN', 'SA', 'DIPOLOG', ',', 'NAGSUSPENSO', 'SA', 'KLASE', ',', 'TRABAHO', 'TUNGOD', 'SA', 'VALENTINE', ''S', 'DAY', 'Mao', 'kana', 'ang', 'gianunsyo', 'sa', 'usa', 'ka', 'pribadong', 'eskwelahan', 'sa', 'Dipolog', 'City', ',', 'Zamboanga', 'del', 'Norte', 'karong', 'Martes', 'human', 'niini', 'gideklarar', 'nga', 'wala'y', 'klase', 'ug', 'trabaho', 'sa', 'Valentine', ''s', 'Day.', 'Nagdeklarar', 'og', '"', 'free', 'day', '"', 'ang', 'St.', 'Vincent', ''s', 'College', 'Inc.', 'sa', 'maong', 'lungsod', 'aron', 'ma-enjoy', 'sa', 'mga', 'estudyante', 'ug', 'empleyado', 'ang', 'Valentine', ''s', 'Day.', 'Giawhag', 'sab', 'sa', 'maong', 'tunghaan', 'ang', 'tanang', 'estudyante', 'ug', 'empleyado', 'nga', 'maghatag', 'og', 'pag-ampo', 'alang', 'sa', 'mga', 'biktima', 'sa', 'linog', 'sa', 'Syria', 'ug', 'Turkey', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility 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, 7, 8, 8, 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, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0]
|
cebuaner
|
4,265
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['I-atak', 'na', 'ang', 'inyong', 'tanang', 'entries', 'karong', 'semanaha', 'tungod', 'karong', 'Sabado', ',', 'Feb.', '18', ',', 'mao', 'ang', '8th', 'ug', 'last', 'weekly', 'draw', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '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, 0, 7, 8, 8, 8, 8, 0, 0]
|
cebuaner
|
4,266
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BROKEN', 'HEART', ',', 'POSIBLENG', 'MAKAMATAY', 'MATUD', 'SA', 'MGA', 'EKSPERTO', 'Gipasabot', 'sa', 'usa', 'ka', 'doctor', 'nga', 'tinuod', 'ang', '"', 'broken', 'heart', 'syndrome', '"', 'nga', 'susama', 'sa', '"', 'extreme', 'emotional', 'stress', '"', 'nga', 'mahimong', 'maggikan', 'usa', 'ka', 'napakyas', 'nga', 'relasyon.', 'Gihisgutan', 'ni', 'Dr.', 'Nicholas', 'Cruz', ',', 'chairperson', 'sa', 'Heart', 'Institute', 'of', 'St.', 'Luke', ''s', 'Medical', 'Center', ',', 'ang', 'bag-ong', 'nadiskobrehang', 'sakit', 'isip', 'usa', 'ka', '"', 'stress-induced', 'cardiomyopathy.', '"', '"', 'Nasisira', ''yung', 'muscle', 'ng', 'puso', 'dahil', 'sa', 'sobrang', 'stress', ',', 'humihina', 'nang', 'biglaan', '...', 'Ang', 'theory', 'nila', ',', 'dahil', 'sa', 'sobrang', 'taas', 'ng', 'adrenaline', ',', 'naaapektuhan', ''yung', 'muscle', 'ng', 'puso', 'at', 'nagiging', 'mahina', ''yung', 'puso', 'kaya', 'tinatawag', 'na', 'broken', 'heart', 'syndrome', ',', '"', 'matod', 'pa', 'ni', 'Dr.', 'Cruz.', '"', 'There', ''s', 'enough', 'oxygen', ',', 'pero', 'sobra', ''yung', 'stress', 'na', 'inabot', 'ng', 'puso', 'dahil', 'sa', 'taas', 'ng', 'adrenaline.', 'Lalo', 'siyang', 'nagtatrabaho', ',', '"', 'dugang', 'pa', 'niya.', 'Sumala', 'pa', 'ni', 'Cruz', ',', 'usa', 'ka', 'teorya', 'nga', 'nagpatin-aw', 'sa', 'maong', 'phenomenon', 'mao', 'nga', 'awtomatic', 'nga', 'mo-respond', 'ang', 'lawas', 'sa', 'mga', 'high-stress', 'situations', 'pinaagi', 'sa', '"', 'pagprotektar', '"', 'sa', 'kasingkasing', 'sa', 'dihang', 'ma-overworked', 'kini.', 'Mahimo', 'kining', 'hinungdan', 'sa', 'kalit', 'nga', '"', 'pagpahulay', '"', 'sa', 'maong', 'vital', 'organ.', 'Kung', 'mahitabo', 'ang', '"', 'broken', 'heart', 'syndrome', ',', '"', 'mahimong', 'makasinati', 'sa', 'mga', 'mosunod', ':', 'magsakit', 'ang', 'dughan', ',', 'paglisod', 'sa', 'ginhawa', ',', 'pagkaluya', ',', 'ug', 'sa', 'ubang', 'kaso', ',', 'mahimong', 'makuyapan'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 1, 2, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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]
|
cebuaner
|
4,267
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['CUDDLE', 'WEATHER', 'KARONG', 'VALENTINE', ''S', 'DAY', 'Magdala', 'og', 'katag-katag', 'nga', 'pag-ulan', 'ug', 'thunderstorms', 'ang', 'trough', 'sa', 'low', 'pressure', 'area', '(', 'LPA', ')', 'ngadto', 'sa', 'Visayas', 'ug', 'Mindanao', 'karong', 'Valentine', ''s', 'Day.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Atmospheric', ',', 'Geophysical', 'and', 'Astronomical', 'Services', 'Administration', '(', 'PAGASA', ')', 'karong', 'adlawa', ',', 'Pebrero', '14', ',', '2023.', 'Sumala', 'pa', 'sa', 'PAGASA', ',', 'mahimong', 'makasinati', 'og', 'kalit', 'nga', 'pagbaha', 'o', 'pagdahili', 'sa', 'yuta', 'ang', 'maong', 'mga', 'lugar', 'tungod', 'sa', 'pag-ulan.', 'Anaa', 'pa', 'sa', 'gawas', 'sa', 'Philippine', 'Area', 'of', 'Responsibility', '(', 'PAR', ')', 'ang', 'LPA', ',', 'apan', 'gibanabanang', 'magdala', 'og', 'hinay', 'ngadto', 'sa', 'kusog', 'nga', 'pag-ulan', 'ang', 'trough', 'niini', 'sa', 'Eastern', 'Visayas', ',', 'Caraga', 'ug', 'Northern', 'Mindanao', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 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, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 7, 8, 8, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 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, 5, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 5, 6, 0]
|
cebuaner
|
4,268
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['BES', ',', 'APIL', 'NA', 'KAY', '4', 'DAYS', 'NALANG', '!', 'Mao', 'kini', 'ang', 'mga', 'pamaagi', 'sa', 'pag-apil', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '2023', '!', 'Join', 'now', 'and', 'get', 'a', 'chance', 'to', 'win', 'P1,000', 'weekly', 'and', 'pwede', 'kang', 'mahimong', 'milyonaryo', 'sa', 'Grand', 'Draw', '!'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,269
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'na', 'sa', 'kapin', '35,000', 'ang', 'kompirmadong', 'namatay', 'sa', 'kusog', 'nga', 'magnitude', '7.8', 'nga', 'linog', 'nga', 'nitay-og', 'sa', 'Turkey', 'ug', 'Syria.', 'Matud', 'pa', 'sa', 'latest', 'nga', 'update', 'sa', 'mga', 'awtoridad', 'sa', 'duha', 'ka', 'nasud', ',', 'anaa', 'na', 'sa', '31,643', 'ang', 'nakalas', 'sa', 'Tukrey', 'samtang', 'nisaka', 'ngadto', 'sa', '3,581', 'nakabsan', 'og', 'kinabuhi', 'sa', 'Syria.', 'Tungod', 'niini', ',', 'nisaka', 'na', 'ngadto', 'sa', '35,224', 'ang', 'suma', 'total', 'sa', 'mga', 'nakalas', 'sa', 'naasoy', 'nga', 'trahedya', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 5, 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, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,270
|
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', 'P1', 'NGA', 'PLITE', 'ALANG', 'SA', 'GIPAABOT', 'NGA', 'SUMMER', 'SEASON', 'Nagtanyag', 'ang', 'AirAsia', 'og', 'P1', 'fare', 'promo', 'alang', 'sa', 'gipaabot', 'nga', 'summer', 'season', 'sa', 'nasud.', 'Mahimong', 'mo-book', 'ang', 'mga', 'pasahero', 'sa', 'P1', 'flights', 'sa', 'Feb.', '13-19', 'aron', 'makapili', 'sa', 'domestic', 'destinations', 'sama', 'sa', 'Cagayan', 'De', 'Oro', ',', 'Boracay', ',', 'Davao', ',', 'Kalibo', 'ug', 'Puerto', 'Princesa.', 'Samtang', 'anaa', 'sa', 'P911', 'ang', 'international', 'flights', 'sama', 'sa', 'Taipei', ',', 'Macao', ',', 'Hong', 'Kong', ',', 'Seoul', ',', 'Singapore', ',', 'Bangkok', ',', 'Osaka', ',', 'ug', 'Tokyo.', 'Ang', 'travel', 'period', 'sa', 'maong', 'promotional', 'flights', 'anaa', 'sa', 'Feb.', '13', 'hangtod', 'sa', 'Nov.', '30', ',', '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, 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, 7, 8, 8, 0, 7, 0, 7, 0, 7, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 8, 0, 7, 0, 7, 0, 7, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,271
|
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', 'mga', 'estudyante', 'sa', 'Negros', 'Oriental', 'State', 'University', 'alang', 'sa', 'unang', 'adlaw', 'sa', 'klase', 'sa', 'second', 'semester', 'sa', 'academic', 'year', '2022-2023', '.'] 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, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,272
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['I-atak', 'na', 'ang', 'inyong', 'tanang', 'entries', 'karong', 'semanaha', 'tungod', 'karong', 'Sabado', ',', 'Feb.', '18', ',', 'mao', 'ang', '8th', 'ug', 'last', 'weekly', 'draw', 'sa', 'Bagong', 'Taon', ',', 'Bagong', 'Milyon', '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, 0, 7, 8, 8, 8, 8, 0, 0]
|
cebuaner
|
4,273
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Nagpagawas', 'ang', 'National', 'Wages', 'and', 'Productivity', 'Commission', 'og', 'price', 'guide', 'karong', 'adlawa', ',', 'Feb.10', ',', 'alang', 'sa', 'mga', 'ganahang', 'mag-side', 'hustle', 'karong', 'Valentine', ''s', 'Day', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 3, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0]
|
cebuaner
|
4,274
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MGA', 'NAKALAS', 'SA', 'KUSOG', 'NGA', 'LINOG', 'SA', 'TURKEY', 'UG', 'SYRIA', ',', 'MOABOT', 'NA', 'SA', 'KAPIN', '21,000', 'Nisaka', 'na', 'ngadto', 'sa', 'kapin', '21,000', 'ka', 'tawo', 'ang', 'namatay', 'tungod', 'sa', 'kusog', 'nga', 'magnitude', '7.8', 'nga', 'linog', 'nga', 'nitay-og', 'sa', 'Turkey', 'ug', 'Syria.', 'Nagpadayon', 'gihapon', 'ang', 'search', 'and', 'rescue', 'operations', 'sa', 'mga', 'awtoridad', 'didto', ',', 'inubanan', 'sa', 'mga', 'rescue', 'teams', 'gikan', 'sa', 'nagkalain-laing', 'nasud.', 'Apan', 'gikahadlukan', 'nga', 'modaghan', 'pa', 'ang', 'mga', 'nakalas', 'tungod', 'sa', 'bugnaw', 'nga', 'panahon', 'ug', 'sa', 'dugay', 'nga', 'panahon', 'nga', 'natanggong', 'sa', 'mga', 'nahugno', 'nga', 'building', 'ang', 'mga', 'biktima', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 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
|
4,275
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'patay'ng', 'lawas', 'sa', 'babayi', 'ang', 'nakit-an', 'sa', 'baybayon', 'sa', 'Sitio', 'Capagongan', ',', 'Barangay', 'Basak', 'sa', 'Zamboanguita', 'mga', 'alas-12', 'sa', 'udto', 'karong', 'adlawa', ',', 'Pebrero', '10', ',', '2023.', 'Sumala', 'pa', 'sa', 'kapulisan', 'sa', 'Zamboanguita', 'nga', 'anaa', 'sa', '27', 'anyos', 'ang', 'babayi', ',', 'kinsa', 'lumolupyo', 'sa', 'Sitio', 'Dalakit', ',', 'Barangay', 'Poblacion', 'sa', 'naasoy', 'nga', 'lungsod.', 'Matod', 'pa', 'sa', 'inisyal', 'nga', 'imbestigayon', ',', 'nakit-an', 'pa', 'ang', 'maong', 'babayi', 'ganihang', 'buntag', 'nga', 'naglakaw', 'sa', 'maong', 'baybayon.', 'Wala', 'kini', 'panganli', 'aron', 'paghatag', 'sab', 'og', 'respeto', 'sa', 'mga', 'tagtungod', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 6, 6, 6, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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]
|
cebuaner
|
4,276
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INDIAN', 'GOV'T', ',', 'GIHANGYO', 'ANG', 'KATAWHAN', 'NGA', 'HALUGON', 'ANG', 'MGA', 'BAKA', 'SA', 'VALENTINE', ''S', 'DAY', 'Gihangyo', 'sa', 'Animal', 'Welfare', 'Board', 'sa', 'India', 'ang', 'mga', 'lungsuranon', 'niini', 'nga', 'markahan', 'ang', 'Valentine', ''s', 'Day', 'karong', 'tuiga', 'dili', 'isip', 'usa', 'ka', 'selebrasyon', 'sa', 'romansa', 'apan', 'isip', '"', 'Cow', 'Hug', 'Day', '"', 'aron', 'pagpalambo', 'sa', 'Hindu', 'values.', 'Sumala', 'pa', 'sa', 'government-run', 'animal', 'welfare', 'department', 'niadtong', 'Miyerkules', ',', 'Pebrero', '8', ',', '2023.', 'Ang', 'Devout', 'Hindus', ',', 'kinsa', 'nagsimba', 'sa', 'mga', 'baka', 'isip', 'balaan', ',', 'nag-ingon', 'nga', 'supak', 'sa', 'tradisyonal', 'nga', 'Indian', 'values', 'ang', 'maong', 'Western', 'holiday', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 7, 8, 8, 0, 0, 3, 4, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 7, 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, 7, 0, 0, 0, 7, 0, 0]
|
cebuaner
|
4,277
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['2', 'PINOY', 'SA', 'TURKEY', ',', 'KOMPIRMADONG', 'PATAY', 'TUNGOD', 'SA', 'KUSOG', 'NGA', 'LINOG', 'NIADTONG', 'LUNES', 'Duha', 'ka', 'mga', 'Pilipino', 'ang', 'kompirmadong', 'namatay', 'sa', 'nahitabong', 'kusog', 'nga', 'linog', 'sa', 'Turkey.', 'Mao', 'kini', 'ang', 'gikompirmar', 'sa', 'Embahada', 'sa', 'Pilipinas', 'sa', 'naasoy', 'nga', 'nasud', 'karong', 'adlawa', ',', 'Pebrero', '10', ',', '2023.', 'Nagpadayon', 'ang', 'grupo', 'sa', 'Pilipinas', 'sa', 'relief', ',', 'rescue', ',', 'and', 'evacuation', 'operations', 'sa', 'mga', 'Pilipino', 'sa', 'southeast', 'Turkey', ',', 'uban', 'sa', 'tabang', 'sa', 'pipila', 'ka', 'mga', 'ahensya', 'ug', 'mga', 'volunteer.', 'Malapuson', 'sab', 'nga', 'gipabakwit', 'sa', 'grupo', 'ang', 'pulo', 'ka', 'mga', 'pamilya', 'gikan', 'sa', 'lungsod', 'sa', 'Antakya', ',', 'Hatay', 'province', ',', 'usa', 'sa', 'mga', 'lungsod', 'nga', 'grabeng', 'naigo', 'sa', 'maong', 'linog.', 'Niadtong', 'Lunes', ',', 'Pebrero', '6', ',', 'gitay-og', 'og', 'magnitude', '7.8', 'nga', 'linog', 'ang', 'Turkey', 'ug', 'Syria', 'nga', 'gisundan', 'sab', 'og', 'mga', 'kusog', 'nga', 'aftershock.', 'Liboan', 'ka', 'mga', 'tawo', 'ang', 'namatay', 'ug', 'naangol', 'sa', 'maong', 'linog.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'sab', 'ang', 'search', 'and', 'rescue', 'operations', 'sa', 'mga', 'awtoridad', 'didto', 'alang', 'sa', 'mga', 'residente', 'nga', 'nadat-ugan', 'sa', 'mga', 'nahugno', 'nga', 'building', 'ug', 'balay', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 3, 0, 5, 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, 7, 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, 5, 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, 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]
|
cebuaner
|
4,278
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['3', 'KA', 'BRAND', 'NEW', 'MINI', 'DUMP', 'TRUCKS', ',', 'GIPALIT', 'ARON', 'MAKATABANG', 'PAGPALAMBO', 'SA', 'PAGKOLEKTA', 'SA', 'BASURA', 'Tulo', 'ka', 'mga', 'brand', 'new', 'nga', '6-wheeler', 'mini', 'dump', 'truck', 'ang', 'bag-ohay', 'lang', 'nga', 'napalit', 'sa', 'City', 'Environment', 'and', 'Natural', 'Resources', 'Office', 'aron', 'makatabang', 'sa', 'pagpalambo', 'sa', 'pagkolekta', 'og', 'basura.', 'Giaprobahan', 'ni', 'Mayor', 'Felipe', 'Remollo', 'ang', 'pagkuha', 'sa', 'tulo', 'ka', 'mga', 'dump', 'trucks', 'ilabi', 'na', 'alang', 'sa', 'pagkolekta', 'og', 'biodegradable', 'ug', 'residual', 'wastes', 'gikan', 'sa', 'mga', 'barangay.', 'Gihatod', 'ang', 'maong', 'mga', 'dump', 'trucks', 'kagahapong', 'adlawa', 'ug', 'nagpahigayon', 'sab', 'og', 'blessing', 'ceremony', 'sa', 'Presidencia', 'grounds.', 'Usa', 'sab', 'ka', 'brand', 'new', 'nga', 'compactor', 'truck', 'ang', 'ihatod', 'puhon', 'human', 'makompleto', 'na', 'ang', 'public', 'bidding', 'process', 'niini.', 'Ipuli', 'kini', 'nga', 'mga', 'bag-ong', 'trucks', 'sa', 'mga', 'karaang', 'units', 'sa', 'City', 'ENRO', 'ug', 'makapalambo', 'sa', 'pagkolekta', 'sa', 'basura.', 'Giawhag', 'sab', 'ni', 'City', 'ENR', 'Officer', 'Engr.', 'Chilvier', 'C.', 'Patrimonio', 'ang', 'mga', 'residente', 'ug', 'tag-iya', 'sa', 'mga', 'establisemento', 'sa', 'negosyo', 'sa', 'pagbuhat', 'og', '"', 'waste', 'segregation', '"', 'nga', 'gimando', 'sa', 'balaod', 'aron', 'makuhaan', 'ang', 'gidaghanon', 'sa', 'basura', 'nga', 'kolektahon', 'ug', 'ilabay', 'sa', 'Central', 'Materials', 'Recovery', 'Facility', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 3, 4, 4, 4, 4, 4, 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, 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 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, 5, 6, 6, 6, 0]
|
cebuaner
|
4,279
|
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', ''colorized', 'photo', ''', 'sa', 'usa', 'ka', 'normal', 'nga', 'adlaw', 'sa', 'pantalan', 'sa', 'dakbayan', 'sa', 'Dumaguete', 'sa', 'Negros', 'Oriental', 'sa', 'wala', 'pa', 'ang', 'WW2', 'niadtong', '1939', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0, 0, 0, 7, 0, 0, 0]
|
cebuaner
|
4,280
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Moabot', 'na', 'sa', 'kapin', '11,200', 'ang', 'kompirmadong', 'namatay', 'sa', 'kusog', 'nga', 'linog', 'nga', 'nitay-og', 'sa', 'Turkey', 'ug', 'Syria', 'niadtong', 'Lunes.', 'Sumala', 'pa', 'sa', 'mga', 'awtoridad', ',', '8,574', 'ka', 'tawo', 'ang', 'namatay', 'sa', 'Turkey', 'samtang', '2,662', 'ang', 'nakalas', 'sa', 'silingang', 'nasud', 'niini', 'nga', 'Syria', 'sa', 'naasoy', 'nga', 'magnitude', '7.8', 'nga', 'linog.', 'Kapin', '55,000', 'ka', 'tawo', 'sab', 'sa', 'duha', 'ka', 'nasud', 'ang', 'naangol.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'ang', 'search', 'and', 'rescue', 'operations', 'sa', 'mga', 'awtoridad', 'didto', 'alang', 'sa', 'mga', 'residente', 'nga', 'nadat-ugan', 'sa', 'mga', 'nahugno', 'nga', 'building', 'ug', 'balay.', 'Gilaumang', 'magpadala', 'na', 'sab', 'ang', 'Pilipinas', 'og', '85-person', 'team', 'ngadto', 'sa', 'Turkey', 'aron', 'motabang', 'sa', 'rescue', 'operations', '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, 5, 0, 5, 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, 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, 5, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,281
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Posibleng', 'makasinati', 'ang', 'nasud', 'og', 'bugnaw', 'nga', 'panahon', 'sugod', 'karon', 'February', '14', ',', '2023', 'tungod', 'sa', 'Northeast', 'Monsoon', 'o', 'Amihan', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,282
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['KASALANG', 'BAYAN', ',', 'GI-RESCHEDULE', 'NGADTO', 'SA', 'JUNE', '16', 'Gi-postpone', 'ang', 'Kasalang', 'Bayan', 'nga', 'gitakda', 'untang', 'ipahigayon', 'karong', 'February', '14', ',', '2023', 'ngadto', 'sa', 'June', '16', ',', '2023.', 'Giuswag', 'kini', 'aron', 'pagtagad', 'sa', 'hangyo', 'nga', 'hatagan', 'og', 'igong', 'panahon', 'ang', 'mga', 'pares', 'nga', 'makompleto', 'ang', 'pagproseso', 'sa', 'ilang', 'mga', 'dokumento', 'aron', 'makaapil', 'sa', 'maong', 'kalihukan.', 'Kaniadto', ',', 'gikompiramar', 'ni', 'Mayor', 'Felipe', 'Remollo', ',', 'kinsa', 'modumala', 'sab', 'sa', 'maong', 'kasal', ',', 'nga', 'dili', 'na', 'kinahanglanon', 'ang', 'pagbayad', 'sa', 'local', 'fees', 'aron', 'makakuha', 'og', 'marriage', 'license', 'o', 'certificate', 'kadtong', 'mga', 'pares', 'nga', 'moapil', 'sa', 'Kasalang', 'Bayan', '2023.', 'Nagpahinumdom', 'sab', 'si', 'Local', 'Civil', 'Registrar', 'Carlo', 'Cual', 'nga', 'kinahanglang', 'ipasa', 'ang', 'tanang', 'dokumento', 'sa', 'adlaw', 'o', 'sa', 'wala', 'pa', 'ang', 'May', '31', ',', '2023', 'sa', 'kadtong', 'mga', 'interesado', 'nga', 'live-in', 'couples', 'ug', 'non-cohabiting', 'parties', 'lakip', 'na', 'ang', 'mga', 'langyaw', 'ug', 'residente', 'gawas', 'sa', 'dakbayan.', 'Ipasa', 'ang', 'tanang', 'kinahanglang', 'requirements', 'sa', 'LCR', ',', 'ug', 'alayon', 'pagdala', 'sa', 'mga', 'sponsors', 'aron', 'makapirma', 'na', 'sila', 'sa', 'marriage', 'license', 'sa', 'wala', 'pa', 'ang', 'adlaw', 'sa', 'kasal.', 'Giawhag', 'sab', 'ang', '30', 'ka', 'mga', 'Barangay', 'Captains', 'sa', 'Dumaguete', 'City', 'sa', 'pag-endorso', 'ug', 'pagtabang', 'sa', 'pagrehistro', 'sa', 'mga', 'interesado', 'nga', 'pares', 'nga', 'moapil', 'sa', 'Kasalang', 'Bayan', 'aron', 'malehitimo', 'ang', 'ilang', 'panag-uban.', 'Alang', 'sa', 'mga', 'pangutana', 'ug', 'dugang', 'detalye', ',', 'mahimong', 'personal', 'nga', 'bisitahon', 'ang', 'Office', 'of', 'the', 'Local', 'Civil', 'Registrar', 'tapad', 'sa', 'Dumaguete', 'City', 'Fire', 'Station', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 7, 8, 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, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 0, 0, 5, 6, 6, 6, 0]
|
cebuaner
|
4,283
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['MANILA', 'GINGANLAN', 'ISIP', '"', 'MOST', 'LOVING', 'CAPITAL', 'CITY', 'IN', 'THE', 'WORLD', '"', 'SA', 'USA', 'KA', 'PAGTUON', 'Nailhan', 'ang', 'mga', 'Pilipino', 'sa', 'daghan', 'nga', 'mga', 'butang', ',', 'lakip', 'na', 'ang', 'pagkamaabiabihon', ',', 'aduna'y', 'lig-on', 'nga', 'relasyon', 'sa', 'pamilya', ',', 'ug', 'pagkamalig-on', 'nga', 'tawo.', 'Apan', 'gibutyag', 'sa', 'usa', 'ka', 'bag-ong', 'pagtuon', 'nga', 'ang', 'mga', 'Pilipino', 'sab', 'ang', 'pipila', 'sa', '"', 'most', 'loving', '"', 'o', 'labing', 'mahigugmaon', 'nga', 'mga', 'tawo', 'sa', 'kalibutan.', 'Sumala', 'pa', 'kini', 'sa', 'bag-ong', 'global', 'nga', 'pagtuon', 'sa', 'Crosswork', 'Solver', 'diin', 'ilang', 'nakita', 'kung', 'unsang', 'mga', 'nasud', 'sa', 'kalibutan', 'ang', 'labing', 'mahigugmaon', 'base', 'sa', 'kung', 'kapila', 'nga', 'beses', 'moingon', 'ang', 'mga', 'tawo', 'og', '"', 'love', 'you', '"', 'sa', 'online.', 'Ang', 'Manila', ',', 'kapita', 'sa', 'Pilipinas', ',', 'mao', 'ang', 'ilang', 'nakita', 'nga', '"', 'most', 'loving', 'capital', 'city', 'in', 'the', 'world', '"', 'nga', 'aduna'y', '1,246', 'nga', 'loving', 'tweets', 'sa', 'kada', '100,000', 'nga', 'gi-post', 'sa', 'mga', 'social', 'media', 'platforms.', 'Gisundan', 'kini', 'sa', 'uban', 'pang', 'mga', 'kapital', 'nga', 'Guatemala', 'City', ',', 'Guatemala', '(', '1,224', ')', ';', 'Luanda', ',', 'Angola', '(', '1,180', ')', ';', 'Jakarta', ',', 'Indonesia', '(', '974', ')', ';', 'ug', 'Mexico', 'City', ',', 'Mexico', '(', '948', ')', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 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, 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, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 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, 5, 6, 6, 6, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 5, 6, 6, 6, 0, 0, 0, 0]
|
cebuaner
|
4,284
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SEN.', 'TULFO', ',', 'GISUGYOT', 'ANG', 'PAGDILI', 'SA', 'MGA', 'BATAN-ON', 'SA', 'PAGGAMIT', 'OG', 'SOCIAL', 'MEDIA', 'ARON', 'PAG-UBOS', 'SA', 'TEENAGE', 'PREGNANCY', 'Gisugyot', 'ni', 'Senador', 'Raffy', 'Tulfo', 'niadtong', 'Martes', 'ang', 'usa', 'ka', 'solusyon', 'aron', 'pagtubag', 'sa', 'teenage', 'pregnancy', ',', 'ug', 'nanawagan', 'sa', 'kooperasyon', 'sa', 'mga', 'ahensya', 'sa', 'gobyerno', 'ug', 'mga', 'aplikasyon', 'sa', 'social', 'media.', 'Giawhag', 'ni', 'Sen.', 'Tulfo', 'ang', 'Department', 'of', 'Education', '(', 'DepEd', ')', ',', 'Philippine', 'National', 'Police', '(', 'PNP', ')', ',', 'ug', 'National', 'Bureau', 'of', 'Investigation', '(', 'NBI', ')', 'nga', 'makigtinabangay', 'sa', 'mga', 'sikat', 'nga', 'platform', 'sama', 'sa', 'Tiktok', ',', 'Facebook', ',', 'Bigo', ',', 'Alua', ',', 'ug', 'OnlyFans', 'aron', 'masiguro', 'nga', 'ang', 'mga', 'tiggamit', 'anaa', 'sa', 'legal', 'nga', 'edad.', 'Parte', 'ang', 'iyang', 'sugyot', 'sa', 'usa', 'ka', 'dako', 'nga', 'estratehiya', 'aron', 'pagpanalipod', 'sa', 'mga', 'batan-on', 'ug', 'pagpa-ubos', 'sa', 'mga', 'teenage', 'pregnancy', 'sa', 'nasud', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 1, 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, 0, 0, 0, 0, 0, 0, 1, 0, 3, 4, 4, 4, 4, 4, 0, 3, 4, 4, 4, 4, 4, 0, 0, 3, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 7, 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]
|
cebuaner
|
4,285
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['IGSUON', 'SA', 'MAYOR', 'SA', 'USA', 'KA', 'LUNGSOD', 'SA', 'NEGROS', 'ORIENTAL', ',', 'NAPALGANG', 'PATAY', 'Napalgang', 'patay', 'ang', 'igsuon', 'sa', 'usa', 'ka', 'mayor', 'sa', 'Negros', 'Oriental', 'niadtong', 'Lunes', 'sa', 'buntag', ',', 'Pebrero', '6', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Don', 'Paulo', 'Zartega', 'Teves', ',', '42', 'anyos', ',', 'ug', 'manghod', 'ni', 'Valencia', 'Mayor', 'Edgar', 'Teves.', 'Sumala', 'pa', 'sa', 'report', 'sa', 'Valencia', 'PNP', ',', 'napalgan', 'ang', 'patay', 'nga', 'lawas', 'ni', 'Teves', 'sa', 'Barangay', 'Calayugan', 'sa', 'naasoy', 'nga', 'lungsod.', 'Giputos', 'og', 'habol', 'ug', 'plastic', 'bag', 'ang', 'lawas', 'sa', 'biktima', 'ug', 'aduna', 'sab', 'samad', 'pinusilan', 'sa', 'ulo.', 'Sa', 'pagkakaron', ',', 'wala', 'pa', 'matino', 'sa', 'mga', 'awtoridad', 'ang', 'motibo', 'sa', 'krimen', 'ug', 'kinsa', 'ang', 'nagpaluyo', 'niini.', 'Dugang', 'pa', 'sa', 'kapulisan', ',', 'wala'y', 'koneksyon', 'si', 'Teves', 'sa', 'gobyerno', 'o', 'lokal', 'nga', 'politika', ',', 'ingon', 'man', 'sa', 'New', 'People', ''s', 'Army', '(', 'NPA', ')', '.', 'Dili', 'sab', 'siya', 'konektado', 'sa', 'militar', 'o', 'kapulisan.', 'Sa', 'usa', 'ka', 'lahi', 'nga', 'pahayag', ',', 'nanawagan', 'ang', 'kanhi', 'gobernador', 'sa', 'Negros', 'Oriental', 'nga', 'si', 'Pryde', 'Henry', 'Teves', 'nga', 'paspasan', 'ang', 'imbestigasyon', 'sa', 'nahitabo', 'sa', 'iyang', 'ig-agaw', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 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, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, 1, 2, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 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, 5, 6, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,286
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['INFLATION', ',', 'PASPAS', 'NGA', 'NISAKA', 'NGADTO', 'SA', '8.7', '%', 'NIADTONG', 'ENERO', 'Paspas', 'nga', 'nisaka', 'ang', 'inflation', 'niadtong', 'Enero', 'tungod', 'sa', 'taas', 'nga', 'abang', ',', 'kuryente', 'ug', 'tubig', ',', 'ug', 'ingon', 'man', 'ang', 'padayong', 'pagsaka', 'sa', 'presyo', 'sa', 'pagkaon', 'ug', 'mga', 'utanon.', 'Mao', 'kini', 'ang', 'gibutyag', 'sa', 'Philippine', 'Statistics', 'Authority', '(', 'PSA', ')', 'niadtong', 'Martes', ',', 'Pebrero', '7', ',', '2023.', 'Nisaka', 'ang', 'consumer', 'price', 'index', 'ngadto', 'sa', '8.7', '%', ',', 'pinakataas', 'sukad', 'niadtong', 'Nobyembre', '2008', 'ug', 'mas', 'paspas', 'kung', 'itandi', 'sa', '8.1', '%', 'nga', 'inflation', 'niadtong', 'Disyembre.', 'Nilapas', 'ang', 'kinatibuk-ang', 'inflation', 'sa', 'Enero', 'sa', 'gibanabana', 'sa', 'Bangko', 'Sentral', 'ng', 'Pilipinas', 'nga', '7.5', '%', 'hangtod', 'sa', '8.3', '%', '.', 'Nilabaw', 'sab', 'kini', 'sa', 'target', 'range', 'sa', 'gobyerno', 'nga', '2', '%', 'hangtod', '4', '%', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of 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, 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, 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]
|
cebuaner
|
4,287
|
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', '"', 'BUY', '1', ',', 'TAKE', '"', 'PROMO', 'ALANG', 'SA', 'VALENTINE', ''S', 'DAY', 'Nagtanyag', 'ang', 'AirAsia', 'Philippines', 'og', '"', 'Buy', '1', ',', 'Take', '1', '"', 'promo', 'karong', 'Valentine', ''s', 'Day', 'sa', 'kantidad', 'nga', 'P158', 'sa', 'domestic', 'ug', 'P828', 'alang', 'sa', 'international', 'destinations.', 'Mahimong', 'ma-avail', 'ang', 'maong', 'promo', 'sa', 'February', '6-12', ',', '2023', ',', 'samtang', 'ang', 'travel', 'period', 'niini', 'hangtod', 'sa', 'September', 'ning', 'tuiga', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 7, 8, 8, 0, 0, 3, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,288
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['HARVARD', 'UNIVERSITY', ',', 'NAGTANYAG', 'OG', 'LIBRENG', '102', 'KA', 'MGA', 'ONLINE', 'COURSES', 'Nagtanyag', 'ang', 'Harvard', 'University', 'og', 'libre', 'nga', 'mga', 'online', 'courses.', 'Mahimong', 'tan-awon', 'sa', 'ilang', 'official', 'website', 'ang', '102', 'ka', 'mga', 'kurso', 'gikan', 'sa', '11', 'ka', 'mga', 'subject', 'areas', 'sama', 'sa', 'arts', ',', 'business', ',', 'computer', 'science', ',', 'data', 'science', ',', 'education', 'and', 'teaching', ',', 'health', 'and', 'medicine', ',', 'humanities', ',', 'mathematics', ',', 'programming', ',', 'science', ',', 'ug', 'social', 'sciences.', 'Aduna'y', 'nagkalain-laing', 'mga', 'schedules', 'ug', 'periods', 'ang', 'mga', 'libreng', 'online', 'courses', ',', 'gikan', 'sa', 'usa', 'ka', 'semana', 'hangtod', 'sa', '12', 'ka', 'semana.', 'Aduna', 'sab', 'sila'y', 'tulo', 'ka', 'difficulty', 'levels', ':', 'introductory', ',', 'intermediate', ',', 'ug', 'advanced.', 'Gikan', 'ana', ',', 'pislita', 'lang', 'ang', 'napili', 'nimo', 'nga', 'programa', 'ug', 'pag-sign', 'up', 'sa', 'usa', 'ka', 'account', 'aron', 'ma-enroll.', 'Tugutan', 'ka', 'sa', 'maong', 'prestihiyosong', 'tunghaan', 'nga', 'makakuha', 'og', 'libreng', 'access', 'sa', 'mga', 'materyales', 'sa', 'matag', 'kurso', 'lakip', 'na', 'ang', 'mga', 'video', 'ug', 'balasahon.', 'Mahimo', 'sab', 'kang', 'mobayad', 'og', 'dugang', 'bayronon', 'aron', 'makakuha', 'og', ''verified', 'certificate', 'of', 'completion', ''', 'nga', 'mahimo', 'nimong', 'ilakip', 'sa', 'imong', 'resume', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
|
4,289
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Manghatag', 'ang', 'Cebu', 'entury', 'Plaza', 'Hotel', 'og', '5', 'break-up', 'paid', 'leaves', 'alang', 'sa', 'mga', 'nasakitan', 'ug', 'gibiyaan', 'nga', 'mga', 'empleyado', 'niini.', '"', 'Dili', 'lalim', 'ang', 'mabiyaan', '!', 'At', 'least', 'maka', 'tabang', 'sa', 'pag', 'move-on', ',', '"', 'sumala', 'pa', 'sa', 'post', 'niini', 'sa', 'ilang', 'Facebook', 'page.', 'Dugang', 'pa', 'nila', ',', 'mao', 'kini', 'ang', 'ilang', 'pamaagi', 'sa', 'paghatag', 'og', 'bili', 'sa', 'ilang', 'minahal', 'nga', 'mga', 'empleyado', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,290
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGROS', 'ORIENTAL', ',', 'GITAY-OG', 'OG', 'MAGNITUDE', '6.9', 'EARTHQUAKE', 'Karong', 'adlawa', 'niadtong', '2012', ',', 'gitay-og', 'og', 'magnitude', '6.9', 'nga', 'linog', 'ang', 'probinsya', 'sa', 'Negros', 'Oriental', 'mga', '11:49', 'sa', 'buntag.', 'Nabati', 'kini', 'sa', 'Dumaguete', 'City', ',', 'apan', 'makita', 'ang', 'labing', 'kadaot', 'niini', 'sa', 'Guihulngan', 'City', ',', 'La', 'Libertad', ',', 'ug', 'Jimalalud.', 'Kaniadto', ',', 'nag-isyu', 'ang', 'Philippine', 'Institute', 'of', 'Volcanology', 'and', 'Seismology', '(', 'PHIVOLCS', ')', 'og', 'level', 'two', 'nga', 'tsunami', 'warning', ',', 'apan', 'wala', 'kini', 'nanawagan', 'og', 'bisan', 'unsang', 'evacuation.', 'Dali', 'nga', 'nangbakwit', 'ang', 'mga', 'residente', 'sa', 'Dumaguete', 'ngadto', 'sa', 'lungsod', 'sa', 'Valencia', 'tungod', 'sa', 'nikaylap', 'nga', 'tsunami', 'scare', '--', 'nga', 'komedyang', 'gitawag', 'nga', '"', 'Chona', 'Mae.', '"', 'Ang', 'maong', 'linog', ',', 'nga', 'hinungdan', 'sa', 'wala', 'pa', 'madiskubrehan', 'kaniadto', 'nga', ''fault', ''', ',', 'nakapatay', 'sa', '51', 'ka', 'mga', 'tawo', 'ug', 'nakaangol', 'sa', 'mga', 'usa', 'ka', 'gatos', 'nga', '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.
|
[5, 6, 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, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 0, 0, 5, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 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]
|
cebuaner
|
4,291
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['91-ANYOS', 'NGA', 'LOLO', ',', 'PATAY', 'HUMAN', 'MALIGSAN', 'SA', 'USA', 'KA', '10-WHEELER', 'TRUCK', 'Patay', 'ang', 'usa', 'ka', 'lolo', 'human', 'kini', 'naligsan', 'sa', 'usa', 'ka', '10-wheeler', 'truck', 'sa', 'Barangay', '9', 'sa', 'Tanjay', 'City', 'mga', '8:35', 'sa', 'buntag', 'karong', 'adlawa', ',', 'Pebrero', '5', ',', '2023.', 'Giila', 'sa', 'kapulisan', 'ang', 'biktima', 'nga', 'si', 'Juanito', 'Importante', 'Gablas', ',', '91', 'anyos', ',', 'ug', 'lumolupyo', 'sa', 'Roxas', 'Street', ',', 'Barangay', 'Poblacion', 'sa', 'Pamplona.', 'Samtang', 'ang', 'nagmaneho', 'sa', 'maong', 'truck', 'mao', 'si', 'Joenard', 'Quizon', 'Ledesma', ',', '37', 'anyos', ',', 'minyo', ',', 'ug', 'residente', 'sa', 'Banayo', 'Nangka', 'sa', 'Bayawan', 'City.', 'Sumala', 'pa', 'sa', 'inisyal', 'nga', 'imbestigasyon', ',', 'naglakaw', 'ang', 'biktima', 'gikan', 'sa', 'Tanjay', 'City', 'Public', 'Market', 'paingon', 'sa', 'pedestrian', 'lane.', 'Sa', 'dihang', 'motabok', 'na', 'unta', 'kini', 'sa', 'dalan', ',', 'aksidente', 'kining', 'nabanggaan', 'ug', 'naligsan', 'ang', 'ulo', 'sa', 'maong', 'trak.', 'Tungod', 'niini', ',', 'nakaangkon', 'og', 'grabeng', 'kadaot', 'sa', 'ulo', 'ang', 'biktima', 'hinungdan', 'sa', 'iyang', 'hinanaling', 'kamatayon.', 'Gidala', 'sa', 'DRRMO', 'personnel', 'ang', 'biktima', 'ngadto', 'sa', 'Tanjay', 'Urgent', 'Care', 'Clinic', 'ug', 'gideklarar', 'nga', 'dead', 'on', 'arrival', '(', 'DOA', ')', 'sa', 'nag-atiman', 'nga', 'doktor.', 'Samtang', 'ang', 'drayber', ',', 'anaa', 'na', 'sa', 'kustodiya', 'sa', 'Tanjay', 'CPS', 'alang', 'sa', 'tukmang', 'disposisyon.', 'Sa', 'pagkakaron', ',', 'nagpadayon', 'pa', 'ang', 'imbestigasyon', 'sa', 'kapulisan', 'bahin', '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 6, 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, 5, 6, 6, 6, 6, 0, 5, 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, 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, 0, 0, 0, 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, 5, 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, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,292
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Usa', 'ka', 'dakong', 'halas', 'ang', 'nakit-an', 'sa', 'Barangay', 'Jilocon', ',', 'San', 'Jose', 'sa', 'Negros', 'Oriental', 'mga', 'alas-2', 'sa', 'hapon', 'kagahapong', 'adlawa', ',', 'Pebrero', '3', ',', '2023.', 'Dali', 'kining', 'gi-report', 'ug', 'girespondehan', 'sa', 'LDRRM', 'Rescue', 'Team', ',', 'PNP', 'San', 'Jose', 'ug', 'MEMRO.', 'Gi-turnover', 'sab', 'kini', 'sa', 'Amlan', 'Zoo', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 0, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 3, 4, 4, 0, 3, 0, 0, 0, 0, 5, 6, 0]
|
cebuaner
|
4,293
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['USA', 'KA', 'TINDAHAN', ',', 'MODAWAT', 'OG', 'SIBUYAS', 'ISIP', 'BAYAD', 'Modawat', 'og', 'sibuyas', 'isip', 'bayad', 'sa', 'ilang', 'mga', 'piniling', 'produkto', 'ang', 'Japan', 'Home', 'Center', ',', 'usa', 'ka', 'sikat', 'nga', 'P88', 'store', 'sa', 'Pilipinas', ',', 'sulod', 'lamang', 'sa', 'usa', 'ka', 'adlaw.', 'Ang', 'maong', 'promo', 'hangtod', 'lamang', 'sa', 'Pebrero', '4', 'sa', 'Panay', 'Avenue', 'branch', 'duol', 'sa', 'Victoria', 'Towers.', 'Mahimong', 'makapili', 'ang', 'mga', 'mamalitay', 'og', 'hangtod', 'sa', 'tulo', 'ka', 'mga', 'butang.', 'Ang', 'usa', 'ka', 'sibuyas', ',', 'katumbas', 'sab', 'sa', 'usa', 'ka', 'butang.', 'Sumala', 'pa', 'sa', 'maong', 'tindahan', ',', 'ilang', 'idonar', 'sa', 'community', 'pantry', 'ang', 'ilang', 'nakolekta', 'nga', 'mga', 'sibuyas', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 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]
|
cebuaner
|
4,294
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['SU', 'NAGPADAYON', 'SA', 'PAGDONAR', 'SA', 'ILANG', 'MGA', 'LIBRO', 'LOOK', ':', 'Nagpadayon', 'ang', 'Silliman', 'University', '(', 'SU', ')', 'sa', 'ilang', 'programa', 'sa', 'pagdonar', 'og', 'mga', 'libro', 'nga', 'SU', 'Libro', ',', 'diin', 'nagsugod', 'kini', 'niadtong', '2017.', 'Sumala', 'pa', 'sa', 'Silliman', 'University', 'Library', ',', 'gidonar', 'kini', 'nga', 'mga', 'libro', 'tungod', 'sa', 'ilang', 'umalabot', 'nga', 'Library', 'Transformation.', 'Inay', 'sila', 'manghatag', ',', 'sila', 'ang', 'nanawagan', 'sa', 'mga', 'nagkalainlaing', 'buhatan', 'sa', 'gobyerno', 'nga', 'hakuton', 'ang', 'mga', 'libro', 'unya', 'iapod-iapod', 'sa', 'mga', 'library', 'sa', 'mga', 'public', 'school', ',', 'ilabi', 'na', 'kadtong', 'anaa', 'sa', 'mga', 'hilit', 'nga', 'lugar.', 'Libre', 'kini', 'nga', 'mga', 'libro', 'alang', 'sa', 'mga', 'library', 'sa', 'ekswelahan', 'ug', 'kominidad', 'ug', 'dili', 'alang', 'sa', 'personal', 'nga', 'paggamit.', 'Sa', 'pagkakaron', ',', 'kapin', '30', 'na', 'ka', 'mga', 'recipients', 'ang', 'anaa', 'sa', 'pila', 'sukad', 'sa', 'niaging', 'semana.', 'Sobra', 'na', 'sab', 'kini', 'sa', 'gidaghanon', 'sa', 'mga', 'libro', 'nga', 'magamit.', 'Apan', 'gisubli', 'sa', 'SU', 'Library', 'nga', 'mopahibalo', 'lang', 'sila', 'sa', 'publiko', 'kung', 'aduna', 'pa'y', 'nabilin', 'nga', 'mga', 'libro', 'alang', 'sa', 'ubang', 'mga', 'benepisyaryo.', 'Dugang', 'pa', 'nila', ',', 'mahimong', 'mopili', 'ug', 'mokuha', 'og', 'mga', 'libro', 'ang', 'mga', 'magtutudlo', 'ug', 'estudyante', 'sa', 'SU', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]
|
cebuaner
|
4,295
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Swerte', 'nga', 'nakuhaan', 'sa', 'usa', 'ka', 'astrophotographer', 'sa', 'Tuguegarao', 'City', 'sa', 'Cagayan', 'ang', 'gitawag', 'nga', '"', 'The', 'Green', 'Comet', '"', 'o', 'Comet', 'C', '/', '2022', 'E3', '(', 'ZTF', ')', 'mga', 'alas-3', 'sa', 'kadlawaon', 'karong', 'adlawa', ',', 'Pebrero', '2', ',', '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, 5, 6, 0, 5, 0, 0, 0, 0, 7, 8, 8, 0, 0, 7, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,296
|
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', 'hulagway', 'sa', 'central', 'market', 'nga', 'giila', 'sab', 'isip', '"', 'rice', 'bowl', '"', 'sa', 'Siaton', 'sa', 'Negros', 'Oriental', 'niadtong', '1971'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: Continuation of a facility entity; 7 = O: Non-entity or other words not falling into the specified categories.
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 6, 0, 0]
|
cebuaner
|
4,297
|
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', 'P22', 'NGA', 'PLITE', 'PAINGON', 'SA', 'JAPAN', 'Nagtanyag', 'ang', 'AirAsia', 'Philippines', 'og', 'P22', 'nga', 'one-way', 'base', 'fare', 'ngadto', 'sa', 'Tokyo', 'isip', 'parte', 'sa', 'ilang', '2.2', 'Red', 'Hot', 'Sale.', 'Niadtong', 'Miyerkules', ',', 'gianunsyo', 'sa', 'AirAsia', 'nga', 'ilang', 'giabrihan', 'ang', 'pagbiyahe', 'gikan', 'sa', 'Manila', 'ngadto', 'sa', 'Tokyo', 'pinaagi', 'sa', 'ruta', 'sa', 'Narita', ',', 'diin', 'panahon', 'sab', 'kini', 'sa', 'mga', 'kapistahan', 'sa', 'Japan', 'sa', 'bulan', 'sa', 'Pebrero.', 'Ang', 'ubang', 'international', 'destinations', 'nga', 'nalakip', 'sa', 'sale', 'mao', 'ang', 'Osaka', ',', 'Taipei', ',', 'Hong', 'Kong', ',', 'Bali', ',', 'ug', 'Kaohsiung.', 'Samtang', 'ang', 'domestic', 'destinations', 'mao', 'ang', 'Caticlan', ',', 'Tagbilaran', ',', 'Bacolod', ',', 'Cagayan', ',', 'ug', 'Iloilo.', 'Hangtod', 'Pebrero', '5', 'ang', 'pag-book', 'sa', 'maong', 'sale', ',', 'samtang', 'aduna', 'kini', 'travel', 'period', 'hangtod', 'Oktubre', '11', ',', '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, 5, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 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, 5, 0, 5, 0, 5, 6, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
cebuaner
|
4,298
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['Gikihaan', 'sa', 'usa', 'ka', 'lalaki', 'sa', 'Singapore', 'ang', 'babayeng', 'iyang', 'naibgan', 'kinsa', 'kuno', 'nang-', '"', 'friendzone', '"', 'niya.', 'Nangayo', 'sa', 'korte', 'ang', 'lalaking', 'si', 'K.', 'Kawshigan', 'og', 'danyos', 'nga', 'SG', '$', '3', 'milyon', '(', 'P124.4', 'milyon', ')', 'sa', 'iyang', 'kanhing', 'crush', 'nga', 'si', 'Nora', 'Tan', 'tungod', 'sa', 'hilabihan', 'kunong', 'kadaot', ',', 'depresyon', ',', 'ug', 'trauma', 'nga', 'gihatag', 'niini', 'sa', 'iyang', 'kinabuhi.', 'Nasakitan', 'kuno', 'pag-ayo', 'si', 'Kawshigan', 'human', 'nga', 'amigo', 'ra', 'kuno', 'ang', 'tan-aw', 'ni', 'Tan', 'kaniya.', 'Nagkaila', 'ang', 'duha', 'niadtong', '2016', ',', 'apan', 'nagsugod', 'ang', 'problema', 'niadtong', '2020', 'human', 'giklaro', 'sa', 'babaye', 'nga', 'amigo', 'ra', 'sila', 'ni', 'Kawshigan', ',', 'apan', 'ang', 'lalaki', 'ni-insistir', 'nga', 'si', 'Tan', 'iya', 'kunong', '"', 'closest', 'friend.', '"', 'Nisulay', 'og', 'distansya', 'si', 'Tan', 'ni', 'Kawshigan', 'ug', 'nangayo', 'og', 'boundaries', 'ngadto', 'sa', 'lalaki.', 'Apan', 'inay', 'mopalayo', ',', 'nangbahad', 'pa', 'hinuon', 'ang', 'lalaki', 'nga', 'iyang', 'kihaan', 'si', 'Tan', 'tungod', 'sa', 'emotional', 'trauma', 'nga', 'gihatag', 'niini', 'kaniya.', 'Gitakda', 'karong', 'Pebrero', '9', 'ang', 'paghusay', 'sa', 'korte', 'nunot', 'sa', 'kasong', 'gipasaka', 'ni', 'Kawshigan', 'batok', 'sa', 'iyang', 'kanhing', 'crush', '.'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of a facility entity; 6 = I-FAS: 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, 1, 2, 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, 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, 1, 0, 0, 0, 0, 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, 1, 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]
|
cebuaner
|
4,299
|
What is the tagged array of these Cebuano tokenized words using the BIO encoding schema for the named entity recognition (NER) task? ['NEGROS', 'OCCIDENTAL', 'GOV.', 'LACSON', ',', 'SULAYAN', 'PAGDANI', 'SI', 'GOV.', 'DEGAMO', 'BAHIN', 'SA', 'PAGTUKOD', 'SA', 'NEGROS', 'ISLAND', 'REGION', 'Gibutyag', 'ni', 'Negros', 'Occidental', 'Gov.', 'Eugenio', 'Jose', 'Lacson', 'niadtong', 'Martes', 'nga', 'dili', 'sayon', 'para', 'niya', 'nga', 'madani', 'si', 'Negros', 'Oriental', 'Gov.', 'Roel', 'Degamo', 'nga', 'mosuporta', 'sa', 'pagtukod', 'sa', 'Negros', 'Island', 'Region', ',', 'diin', 'lakip', 'ang', 'Siquijor', ',', 'apan', 'iya', 'kunong', 'sulayan.', 'Gilaoman', 'sila', 'si', 'Degamo', 'ug', 'Lacson', 'niadtong', 'Martes', 'nga', 'magtakda', 'og', 'petsa', 'alang', 'sa', 'ilang', 'panagkita.', 'Tumong', 'ni', 'Lacson', 'nga', 'makigkita', 'ni', 'Degamo', 'aron', 'iyang', 'sulayan', 'kung', 'mahimo', 'bang', 'mausob', 'ang', 'desisyon', 'niini.', 'Kaniadto', ',', 'gipahibalo', 'ni', 'Degamo', 'sa', 'publiko', 'nga', 'supak', 'siya', 'sa', 'pagtukod', 'sa', 'NIR.', 'Giduso', 'ni', 'Lacson', 'ug', 'tanang', 'representante', 'sa', 'Negros', 'Occidental', 'ug', 'Oriental', 'ug', 'Siquijor', 'ang', 'pagtukod', 'sa', 'bag-ong', 'rehiyo'] Use the following schema: 1 = B-WIS: Beginning of a tourism-related entity; 2 = I-WIS: Continuation of a tourism-related entity; 3 = B-LOC: Beginning of a location entity; 4 = I-LOC: Continuation of a location entity; 5 = B-FAS: Beginning of 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, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 5, 6, 6, 0, 0, 5, 6, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 1, 2, 0, 0, 0, 0, 0, 5, 6, 6, 0, 0, 0, 0, 5, 0, 0, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 6, 0, 5, 0, 5, 0, 0, 0, 0, 0]
|
cebuaner
|
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