{"id":"https://openalex.org/W2970601365","doi":"https://doi.org/10.18653/v1/w19-5044","title":"WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference","display_name":"WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2970601365","doi":"https://doi.org/10.18653/v1/w19-5044","mag":"2970601365"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w19-5044","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5044","pdf_url":"https://www.aclweb.org/anthology/W19-5044.pdf","source":null,"license":null,"license_id":null,"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":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W19-5044.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036524439","display_name":"Zhaofeng Wu","orcid":"https://orcid.org/0000-0002-1470-5648"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhaofeng Wu","raw_affiliation_strings":["School of CSE University of Washington"],"affiliations":[{"raw_affiliation_string":"School of CSE University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381754","display_name":"Yan Song","orcid":"https://orcid.org/0000-0002-2849-2962"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Song","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043900893","display_name":"Sicong Huang","orcid":"https://orcid.org/0000-0003-0854-4734"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sicong Huang","raw_affiliation_strings":["Department of ECE University of Washington"],"affiliations":[{"raw_affiliation_string":"Department of ECE University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024080570","display_name":"Yuanhe Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanhe Tian","raw_affiliation_strings":["Department of Linguistics University of Washington"],"affiliations":[{"raw_affiliation_string":"Department of Linguistics University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100676786","display_name":"Fei Xia","orcid":"https://orcid.org/0009-0002-4609-9950"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Xia","raw_affiliation_strings":["Department of Linguistics University of Washington"],"affiliations":[{"raw_affiliation_string":"Department of Linguistics University of Washington","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5036524439"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":1.1201,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.83898902,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9987999796867371,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8566393852233887},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6726566553115845},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6579664945602417},{"id":"https://openalex.org/keywords/premise","display_name":"Premise","score":0.6453244686126709},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5979419946670532},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.582944393157959},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5790217518806458},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5589421391487122},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5401341915130615},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5365270376205444},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4928438663482666},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47975954413414},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.45288658142089844},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.4400220513343811},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44000911712646484},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4244539141654968},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34059998393058777},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.2106235921382904}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8566393852233887},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6726566553115845},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6579664945602417},{"id":"https://openalex.org/C2778023277","wikidata":"https://www.wikidata.org/wiki/Q321703","display_name":"Premise","level":2,"score":0.6453244686126709},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5979419946670532},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.582944393157959},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5790217518806458},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5589421391487122},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5401341915130615},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5365270376205444},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4928438663482666},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47975954413414},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.45288658142089844},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.4400220513343811},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44000911712646484},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4244539141654968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34059998393058777},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2106235921382904},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w19-5044","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5044","pdf_url":"https://www.aclweb.org/anthology/W19-5044.pdf","source":null,"license":null,"license_id":null,"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/w19-5044","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5044","pdf_url":"https://www.aclweb.org/anthology/W19-5044.pdf","source":null,"license":null,"license_id":null,"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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2970601365.pdf","grobid_xml":"https://content.openalex.org/works/W2970601365.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1647671624","https://openalex.org/W1840435438","https://openalex.org/W2100805904","https://openalex.org/W2101105183","https://openalex.org/W2143017621","https://openalex.org/W2154652894","https://openalex.org/W2172734211","https://openalex.org/W2228826686","https://openalex.org/W2250539671","https://openalex.org/W2250790822","https://openalex.org/W2295072214","https://openalex.org/W2396881363","https://openalex.org/W2413794162","https://openalex.org/W2493916176","https://openalex.org/W2608787653","https://openalex.org/W2620811048","https://openalex.org/W2742049800","https://openalex.org/W2745750801","https://openalex.org/W2752582418","https://openalex.org/W2768114048","https://openalex.org/W2798416089","https://openalex.org/W2804552794","https://openalex.org/W2808633496","https://openalex.org/W2888120268","https://openalex.org/W2889747665","https://openalex.org/W2890961898","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2913352150","https://openalex.org/W2923014074","https://openalex.org/W2925863688","https://openalex.org/W2946359678","https://openalex.org/W2946571662","https://openalex.org/W2946581011","https://openalex.org/W2952989203","https://openalex.org/W2953320089","https://openalex.org/W2962739339","https://openalex.org/W2962765866","https://openalex.org/W2962979827","https://openalex.org/W2963077723","https://openalex.org/W2963159690","https://openalex.org/W2963238274","https://openalex.org/W2963310665","https://openalex.org/W2963341956","https://openalex.org/W2963355447","https://openalex.org/W2963716420","https://openalex.org/W2963846996","https://openalex.org/W2963854351","https://openalex.org/W2963918774","https://openalex.org/W2964059756","https://openalex.org/W2964121744","https://openalex.org/W2964303116","https://openalex.org/W2970986790","https://openalex.org/W3106224367"],"related_works":["https://openalex.org/W2367925007","https://openalex.org/W1577263324","https://openalex.org/W3015724364","https://openalex.org/W4288263119","https://openalex.org/W2967994095","https://openalex.org/W2900126711","https://openalex.org/W4285240985","https://openalex.org/W4225162083","https://openalex.org/W3202115945","https://openalex.org/W2542958340"],"abstract_inverted_index":{"Natural":[0],"language":[1],"inference":[2],"(NLI)":[3],"is":[4,9],"challenging,":[5],"especially":[6],"when":[7,87],"it":[8],"applied":[10],"to":[11,26,59,74],"technical":[12],"domains":[13],"such":[14],"as":[15,47],"biomedical":[16,27],"settings.":[17],"In":[18],"this":[19,37],"paper,":[20],"we":[21,66,81],"propose":[22],"a":[23,43,52,56],"hybrid":[24],"approach":[25],"NLI":[28],"where":[29],"different":[30,71],"types":[31],"of":[32,70,116],"information":[33],"are":[34],"exploited":[35],"for":[36],"task.":[38],"Our":[39],"base":[40,72],"model":[41],"includes":[42],"pre-trained":[44],"text":[45],"encoder":[46,54,58],"the":[48,68,88,97,105,109,113,117],"core":[49],"component,":[50],"and":[51,55,62],"syntax":[53],"feature":[57],"capture":[60],"syntactic":[61],"domain-specific":[63],"information.":[64],"Then":[65],"combine":[67],"output":[69],"models":[73,103],"form":[75],"more":[76],"powerful":[77],"ensemble":[78],"models.":[79],"Finally,":[80],"design":[82],"two":[83],"conflict":[84],"resolution":[85],"strategies":[86],"test":[89,114],"data":[90],"contain":[91],"multiple":[92],"(premise,":[93],"hypothesis)":[94],"pairs":[95],"with":[96],"same":[98],"premise.":[99],"We":[100],"train":[101],"our":[102],"on":[104,112],"MedNLI":[106],"dataset,":[107],"yielding":[108],"best":[110],"performance":[111],"set":[115],"MEDIQA":[118],"2019":[119],"Task":[120],"1.":[121]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}