{"id":"https://openalex.org/W4283792257","doi":"https://doi.org/10.3390/make4030030","title":"Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset","display_name":"Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset","publication_year":2022,"publication_date":"2022-07-04","ids":{"openalex":"https://openalex.org/W4283792257","doi":"https://doi.org/10.3390/make4030030"},"language":"en","primary_location":{"id":"doi:10.3390/make4030030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make4030030","pdf_url":"https://www.mdpi.com/2504-4990/4/3/30/pdf?version=1656917033","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/4/3/30/pdf?version=1656917033","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023679437","display_name":"Nontakan Nuntachit","orcid":"https://orcid.org/0000-0002-0978-7810"},"institutions":[{"id":"https://openalex.org/I48076826","display_name":"Chiang Mai University","ror":"https://ror.org/05m2fqn25","country_code":"TH","type":"education","lineage":["https://openalex.org/I48076826"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Nontakan Nuntachit","raw_affiliation_strings":["Data Science Consortium, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand","Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand"],"raw_orcid":"https://orcid.org/0000-0002-0978-7810","affiliations":[{"raw_affiliation_string":"Data Science Consortium, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand","institution_ids":["https://openalex.org/I48076826"]},{"raw_affiliation_string":"Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand","institution_ids":["https://openalex.org/I48076826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025472243","display_name":"Prompong Sugunnasil","orcid":"https://orcid.org/0000-0002-5805-0866"},"institutions":[{"id":"https://openalex.org/I48076826","display_name":"Chiang Mai University","ror":"https://ror.org/05m2fqn25","country_code":"TH","type":"education","lineage":["https://openalex.org/I48076826"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Prompong Sugunnasil","raw_affiliation_strings":["College of Art, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"],"raw_orcid":"https://orcid.org/0000-0002-5805-0866","affiliations":[{"raw_affiliation_string":"College of Art, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand","institution_ids":["https://openalex.org/I48076826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025472243"],"corresponding_institution_ids":["https://openalex.org/I48076826"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.589,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.65590929,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"4","issue":"3","first_page":"641","last_page":"664"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8358511924743652},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6532621383666992},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.6263793110847473},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.571512758731842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5576531291007996},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.48588865995407104},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.46167680621147156},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4361982047557831},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34955596923828125},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.07434791326522827}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8358511924743652},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6532621383666992},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.6263793110847473},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.571512758731842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5576531291007996},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.48588865995407104},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.46167680621147156},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4361982047557831},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34955596923828125},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.07434791326522827},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make4030030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make4030030","pdf_url":"https://www.mdpi.com/2504-4990/4/3/30/pdf?version=1656917033","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:cb3715c5317e40a6af4b16084a889789","is_oa":true,"landing_page_url":"https://doaj.org/article/cb3715c5317e40a6af4b16084a889789","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 4, Iss 3, Pp 641-664 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make4030030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make4030030","pdf_url":"https://www.mdpi.com/2504-4990/4/3/30/pdf?version=1656917033","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.7200000286102295,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283792257.pdf","grobid_xml":"https://content.openalex.org/works/W4283792257.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W652269744","https://openalex.org/W1566289585","https://openalex.org/W1840435438","https://openalex.org/W2064675550","https://openalex.org/W2108281845","https://openalex.org/W2396881363","https://openalex.org/W2725671465","https://openalex.org/W2777746208","https://openalex.org/W2888120268","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2962739339","https://openalex.org/W2963716420","https://openalex.org/W2963846996","https://openalex.org/W2970200208","https://openalex.org/W2970641574","https://openalex.org/W2970771982","https://openalex.org/W3034999214","https://openalex.org/W3035390927","https://openalex.org/W3037025384","https://openalex.org/W3046375318","https://openalex.org/W3081777177","https://openalex.org/W3093277491","https://openalex.org/W3098835531","https://openalex.org/W3099215402","https://openalex.org/W3103459240","https://openalex.org/W3105753785","https://openalex.org/W3160137267","https://openalex.org/W3198651167","https://openalex.org/W3201906559","https://openalex.org/W3211196264","https://openalex.org/W4220660245","https://openalex.org/W4225344339","https://openalex.org/W4226367850","https://openalex.org/W4318766763","https://openalex.org/W4385245566","https://openalex.org/W6676568861","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W2381570729","https://openalex.org/W1976205134","https://openalex.org/W4248336175","https://openalex.org/W2031260042","https://openalex.org/W2391445434","https://openalex.org/W3009369890","https://openalex.org/W4312490297","https://openalex.org/W3098003361"],"abstract_inverted_index":{"The":[0,112],"COVID-19":[1,18,155],"pandemic":[2],"has":[3,19,126],"impacted":[4],"daily":[5],"lives":[6],"around":[7],"the":[8,12,33,55,61,67,76,94,135,148,159,170],"globe.":[9],"Since":[10],"2019,":[11],"amount":[13],"of":[14,32,43,129],"literature":[15],"focusing":[16],"on":[17,54],"risen":[20],"exponentially.":[21],"However,":[22],"it":[23,141,163],"is":[24,132,142],"almost":[25],"impossible":[26],"for":[27],"humans":[28],"to":[29,70,99,154],"read":[30],"all":[31],"studies":[34],"and":[35,74,89,118,150],"classify":[36],"them.":[37],"This":[38],"article":[39],"proposes":[40],"a":[41,49,101],"method":[42],"making":[44],"an":[45,127],"unsupervised":[46],"model":[47,114],"called":[48],"zero-shot":[50],"classification":[51,171],"model,":[52,105],"based":[53],"pre-trained":[56],"BERT":[57],"model.":[58],"We":[59,91],"used":[60,84],"CORD-19":[62],"dataset":[63],"in":[64],"conjunction":[65],"with":[66],"LitCovid":[68],"database":[69],"construct":[71],"new":[72],"vocabulary":[73,151],"prepare":[75],"test":[77],"dataset.":[78],"For":[79],"NLI":[80],"downstream":[81],"task,":[82],"we":[83,145],"three":[85],"corpora:":[86],"SNLI,":[87],"MultiNLI,":[88],"MedNLI.":[90],"significantly":[92],"reduced":[93],"training":[95],"time":[96],"by":[97,138],"98.2639%":[98],"build":[100],"task-specific":[102],"machine":[103],"learning":[104],"using":[106],"only":[107],"one":[108],"Nvidia":[109],"Tesla":[110],"V100.":[111],"final":[113],"can":[115],"run":[116],"faster":[117],"use":[119],"fewer":[120],"resources":[121],"than":[122,134],"its":[123],"comparators.":[124],"It":[125],"accuracy":[128,137],"27.84%,":[130],"which":[131],"lower":[133],"best-achieved":[136],"6.73%,":[139],"but":[140],"comparable.":[143],"Finally,":[144],"identified":[146],"that":[147,166],"tokenizer":[149],"more":[152],"specific":[153],"could":[156],"not":[157],"outperform":[158],"generalized":[160],"ones.":[161],"Additionally,":[162],"was":[164],"found":[165],"BART":[167],"architecture":[168],"affects":[169],"results.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2022-07-05T00:00:00"}