{"id":"https://openalex.org/W2970468244","doi":"https://doi.org/10.18653/v1/w19-5043","title":"Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model","display_name":"Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2970468244","doi":"https://doi.org/10.18653/v1/w19-5043","mag":"2970468244"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w19-5043","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5043","pdf_url":"https://www.aclweb.org/anthology/W19-5043.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th BioNLP Workshop and Shared Task","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W19-5043.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072935465","display_name":"Jiin Nam","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jiin Nam","raw_affiliation_strings":["AI Core Team Samsung Research Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"AI Core Team Samsung Research Seoul, Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101424021","display_name":"Seunghyun Yoon","orcid":"https://orcid.org/0000-0002-7262-3579"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seunghyun Yoon","raw_affiliation_strings":["Dept. ECE Seoul National University Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dept. ECE Seoul National University Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077832834","display_name":"Kyomin Jung","orcid":"https://orcid.org/0000-0003-2547-7051"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyomin Jung","raw_affiliation_strings":["Dept. ECE Seoul National University Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dept. ECE Seoul National University Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5072935465"],"corresponding_institution_ids":["https://openalex.org/I2250650973"],"apc_list":null,"apc_paid":null,"fwci":0.289,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66822974,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"406","last_page":"414"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9879000186920166,"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":0.9833999872207642,"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":0.8579102754592896},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.7844510078430176},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7774444222450256},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7304222583770752},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7109429836273193},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6390953063964844},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5203009247779846},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5150561332702637},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4516139328479767},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35063350200653076},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.06859597563743591}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8579102754592896},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.7844510078430176},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7774444222450256},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7304222583770752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7109429836273193},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6390953063964844},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5203009247779846},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5150561332702637},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4516139328479767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35063350200653076},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.06859597563743591},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w19-5043","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5043","pdf_url":"https://www.aclweb.org/anthology/W19-5043.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th BioNLP Workshop and Shared Task","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w19-5043","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5043","pdf_url":"https://www.aclweb.org/anthology/W19-5043.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th BioNLP Workshop and Shared Task","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8700000047683716,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1103364132","display_name":null,"funder_award_id":"10073144","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"},{"id":"https://openalex.org/G8582691448","display_name":null,"funder_award_id":"MOTIE, Korea","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"}],"funders":[{"id":"https://openalex.org/F4320321292","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542"},{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2970468244.pdf","grobid_xml":"https://content.openalex.org/works/W2970468244.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1832693441","https://openalex.org/W1840435438","https://openalex.org/W2095705004","https://openalex.org/W2152429541","https://openalex.org/W2165698076","https://openalex.org/W2396881363","https://openalex.org/W2608787653","https://openalex.org/W2888120268","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2923014074","https://openalex.org/W2951528484","https://openalex.org/W2962739339","https://openalex.org/W2963090765","https://openalex.org/W2963310665","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963475411","https://openalex.org/W2963846996","https://openalex.org/W2963854351","https://openalex.org/W2963918774","https://openalex.org/W2964121744","https://openalex.org/W2970229707","https://openalex.org/W2970344931","https://openalex.org/W2970368801","https://openalex.org/W2970414946","https://openalex.org/W2970645034","https://openalex.org/W2970952344","https://openalex.org/W2970986790","https://openalex.org/W2971007555","https://openalex.org/W2971193466","https://openalex.org/W3100271366","https://openalex.org/W3147159644","https://openalex.org/W4300822525","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W4321636575","https://openalex.org/W1986418932","https://openalex.org/W2357796999","https://openalex.org/W2169518243","https://openalex.org/W2045526782","https://openalex.org/W2741131631","https://openalex.org/W1569283511","https://openalex.org/W2156919374","https://openalex.org/W2944691285"],"abstract_inverted_index":{"While":[0],"deep":[1],"learning":[2,79],"techniques":[3],"have":[4],"shown":[5],"promising":[6],"results":[7,88],"in":[8,41,48,80,100,117,131],"many":[9],"natural":[10,103],"language":[11,36,75,104],"processing":[12],"(NLP)":[13],"tasks,":[14],"it":[15],"has":[16],"not":[17],"been":[18],"widely":[19],"applied":[20],"to":[21,127],"the":[22,31,42,54,84,90,93,110,114,135],"clinical":[23,43,85],"domain.":[24,86,137],"The":[25],"lack":[26],"of":[27,34,53,92,113],"large":[28],"datasets":[29],"and":[30,39,77,119],"pervasive":[32],"use":[33],"domain-specific":[35],"(i.e.":[37],"abbreviations":[38],"acronyms)":[40],"domain":[44,102],"causes":[45],"slower":[46],"progress":[47],"NLP":[49,56],"tasks":[50],"than":[51],"that":[52,67],"general":[55],"tasks.":[57],"To":[58],"fill":[59],"this":[60],"gap,":[61],"we":[62,108],"employ":[63],"word/subword-level":[64],"based":[65],"models":[66,76,133],"adopt":[68],"large-scale":[69],"data-driven":[70],"methods":[71,95],"such":[72],"as":[73],"pretrained":[74],"transfer":[78],"analyzing":[81],"text":[82],"for":[83,134],"Empirical":[87],"demonstrate":[89],"superiority":[91],"proposed":[94,115],"by":[96],"achieving":[97],"90.6%":[98],"accuracy":[99],"medical":[101,136],"inference":[105],"task.":[106],"Furthermore,":[107],"inspect":[109],"independent":[111],"strengths":[112],"approaches":[116],"quantitative":[118],"qualitative":[120],"manners.":[121],"This":[122],"analysis":[123],"will":[124],"help":[125],"researchers":[126],"select":[128],"necessary":[129],"components":[130],"building":[132]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
