{"id":"https://openalex.org/W2970952344","doi":"https://doi.org/10.18653/v1/w19-5053","title":"UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain","display_name":"UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2970952344","doi":"https://doi.org/10.18653/v1/w19-5053","mag":"2970952344"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w19-5053","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5053","pdf_url":"https://www.aclweb.org/anthology/W19-5053.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":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W19-5053.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067597021","display_name":"Noha S. Tawfik","orcid":"https://orcid.org/0009-0007-6968-2491"},"institutions":[{"id":"https://openalex.org/I193662353","display_name":"Utrecht University","ror":"https://ror.org/04pp8hn57","country_code":"NL","type":"education","lineage":["https://openalex.org/I193662353"]},{"id":"https://openalex.org/I59272784","display_name":"Arab Academy for Science, Technology, and Maritime Transport","ror":"https://ror.org/0004vyj87","country_code":"EG","type":"education","lineage":["https://openalex.org/I59272784"]}],"countries":["EG","NL"],"is_corresponding":true,"raw_author_name":"Noha Tawfik","raw_affiliation_strings":["Utrecht University 3584CC Utrecht, The Netherlands","Arab Academy for Science & Technology Alexandria 1029, Egypt"],"affiliations":[{"raw_affiliation_string":"Utrecht University 3584CC Utrecht, The Netherlands","institution_ids":["https://openalex.org/I193662353"]},{"raw_affiliation_string":"Arab Academy for Science & Technology Alexandria 1029, Egypt","institution_ids":["https://openalex.org/I59272784"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033880574","display_name":"Marco Spruit","orcid":"https://orcid.org/0000-0002-9237-221X"},"institutions":[{"id":"https://openalex.org/I59272784","display_name":"Arab Academy for Science, Technology, and Maritime Transport","ror":"https://ror.org/0004vyj87","country_code":"EG","type":"education","lineage":["https://openalex.org/I59272784"]},{"id":"https://openalex.org/I193662353","display_name":"Utrecht University","ror":"https://ror.org/04pp8hn57","country_code":"NL","type":"education","lineage":["https://openalex.org/I193662353"]}],"countries":["EG","NL"],"is_corresponding":false,"raw_author_name":"Marco Spruit","raw_affiliation_strings":["Utrecht University 3584 CC Utrecht, The Netherlands","Arab Academy for Science & Technology Alexandria 1029, Egypt","Utrecht University 3584CC Utrecht, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Utrecht University 3584 CC Utrecht, The Netherlands","institution_ids":["https://openalex.org/I193662353"]},{"raw_affiliation_string":"Arab Academy for Science & Technology Alexandria 1029, Egypt","institution_ids":["https://openalex.org/I59272784"]},{"raw_affiliation_string":"Utrecht University 3584CC Utrecht, The Netherlands","institution_ids":["https://openalex.org/I193662353"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5067597021"],"corresponding_institution_ids":["https://openalex.org/I193662353","https://openalex.org/I59272784"],"apc_list":null,"apc_paid":null,"fwci":1.1201,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.83910907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"493","last_page":"499"},"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":0.9990000128746033,"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":0.9916999936103821,"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":0.7707480192184448},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7600017189979553},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7397412657737732},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7054833173751831},{"id":"https://openalex.org/keywords/textual-entailment","display_name":"Textual entailment","score":0.6844203472137451},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.666174054145813},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6330429911613464},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5899021029472351},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5423274040222168},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5372459888458252},{"id":"https://openalex.org/keywords/logical-consequence","display_name":"Logical consequence","score":0.44700127840042114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33385181427001953},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23124495148658752},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0660332441329956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7707480192184448},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7600017189979553},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7397412657737732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7054833173751831},{"id":"https://openalex.org/C95318506","wikidata":"https://www.wikidata.org/wiki/Q6588467","display_name":"Textual entailment","level":3,"score":0.6844203472137451},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.666174054145813},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6330429911613464},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5899021029472351},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5423274040222168},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5372459888458252},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.44700127840042114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33385181427001953},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23124495148658752},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0660332441329956},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w19-5053","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5053","pdf_url":"https://www.aclweb.org/anthology/W19-5053.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/w19-5053","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5053","pdf_url":"https://www.aclweb.org/anthology/W19-5053.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","id":"https://metadata.un.org/sdg/4","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2970952344.pdf","grobid_xml":"https://content.openalex.org/works/W2970952344.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W143623734","https://openalex.org/W1840435438","https://openalex.org/W2101234009","https://openalex.org/W2154474435","https://openalex.org/W2396881363","https://openalex.org/W2574669284","https://openalex.org/W2620811048","https://openalex.org/W2746012317","https://openalex.org/W2786948038","https://openalex.org/W2794557536","https://openalex.org/W2863750952","https://openalex.org/W2888120268","https://openalex.org/W2896457183","https://openalex.org/W2922551710","https://openalex.org/W2925863688","https://openalex.org/W2951877047","https://openalex.org/W2963716420","https://openalex.org/W2970986790","https://openalex.org/W2972987451","https://openalex.org/W4211148418"],"related_works":["https://openalex.org/W2169644218","https://openalex.org/W12963412","https://openalex.org/W2250460949","https://openalex.org/W3158371345","https://openalex.org/W3141423438","https://openalex.org/W2071098659","https://openalex.org/W2627035043","https://openalex.org/W4385571113","https://openalex.org/W2937401546","https://openalex.org/W3030695269"],"abstract_inverted_index":{"This":[0],"article":[1],"describes":[2],"the":[3,6,11,65,83,98],"participation":[4],"of":[5,28,59],"UU":[7],"TAILS":[8],"team":[9,44],"in":[10,20,46],"2019":[12],"MEDIQA":[13],"challenge":[14,26],"intended":[15],"to":[16],"improve":[17],"domain-specific":[18],"models":[19,69],"medical":[21,31],"and":[22,39,49,51,61,78],"clinical":[23],"NLP.":[24],"The":[25,68],"consists":[27],"3":[29],"tasks:":[30],"language":[32],"inference":[33],"(NLI),":[34],"recognizing":[35],"textual":[36],"entailment":[37],"(RQE)":[38],"question":[40],"answering":[41],"(QA).":[42],"Our":[43],"participated":[45],"tasks":[47],"1":[48,73],"2":[50],"our":[52],"best":[53],"runs":[54],"achieved":[55],"a":[56,88],"performance":[57],"accuracy":[58],"0.852":[60],"0.584":[62],"respectively":[63],"for":[64,71],"test":[66],"sets.":[67],"proposed":[70],"task":[72],"relied":[74],"on":[75,94],"BERT":[76],"embeddings":[77,95],"different":[79],"ensemble":[80],"techniques.":[81],"For":[82],"RQE":[84],"task,":[85],"we":[86],"trained":[87],"traditional":[89],"multilayer":[90],"perceptron":[91],"network":[92],"based":[93],"generated":[96],"by":[97],"universal":[99],"sentence":[100],"encoder.":[101]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}