{"id":"https://openalex.org/W2952958555","doi":"https://doi.org/10.18653/v1/p19-2055","title":"Deep Neural Models for Medical Concept Normalization in User-Generated Texts","display_name":"Deep Neural Models for Medical Concept Normalization in User-Generated Texts","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2952958555","doi":"https://doi.org/10.18653/v1/p19-2055","mag":"2952958555"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-2055","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-2055","pdf_url":null,"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 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.18653/v1/p19-2055","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zulfat Miftahutdinov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zulfat Miftahutdinov","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Elena Tutubalina","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elena Tutubalina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4213,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.86878416,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"393","last_page":"399"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9998000264167786,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9998000264167786,"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/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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9983999729156494,"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/normalization","display_name":"Normalization (sociology)","score":0.5917999744415283},{"id":"https://openalex.org/keywords/terminology","display_name":"Terminology","score":0.5608000159263611},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5479999780654907},{"id":"https://openalex.org/keywords/medical-terminology","display_name":"Medical terminology","score":0.4627000093460083},{"id":"https://openalex.org/keywords/unified-medical-language-system","display_name":"Unified Medical Language System","score":0.44929999113082886},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4171999990940094},{"id":"https://openalex.org/keywords/semantic-network","display_name":"Semantic network","score":0.3418999910354614},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.3398999869823456},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3327000141143799}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7484999895095825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7166000008583069},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6826000213623047},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5917999744415283},{"id":"https://openalex.org/C547195049","wikidata":"https://www.wikidata.org/wiki/Q1725664","display_name":"Terminology","level":2,"score":0.5608000159263611},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5479999780654907},{"id":"https://openalex.org/C511227900","wikidata":"https://www.wikidata.org/wiki/Q1192539","display_name":"Medical terminology","level":2,"score":0.4627000093460083},{"id":"https://openalex.org/C69505689","wikidata":"https://www.wikidata.org/wiki/Q455338","display_name":"Unified Medical Language System","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4171999990940094},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3398999869823456},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.336899995803833},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3327000141143799},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C67277372","wikidata":"https://www.wikidata.org/wiki/Q7449085","display_name":"Semantic role labeling","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/p19-2055","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-2055","pdf_url":null,"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 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1907.07972","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.07972","pdf_url":"https://arxiv.org/pdf/1907.07972","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-2055","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-2055","pdf_url":null,"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 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"consider":[4],"the":[5,11,32,36,61,65,114,118,128],"medical":[6,48],"concept":[7,25],"normalization":[8],"problem,":[9],"i.e.,":[10],"problem":[12,78],"of":[13,67,98,117,127],"mapping":[14],"a":[15,20,24,27,44,75],"health-related":[16],"entity":[17,119],"mention":[18,120],"in":[19,26,35,64],"free-form":[21],"text":[22],"to":[23,31,94],"controlled":[28],"vocabulary,":[29],"usually":[30],"standard":[33],"thesaurus":[34],"Unified":[37],"Medical":[38],"Language":[39],"System":[40],"(UMLS).":[41],"This":[42],"is":[43,50],"challenging":[45],"task":[46],"since":[47],"terminology":[49],"very":[51],"different":[52,107],"when":[53],"coming":[54],"from":[55,60],"health":[56],"care":[57],"professionals":[58],"or":[59],"general":[62],"public":[63],"form":[66],"social":[68,99],"media":[69,100],"texts.":[70],"We":[71],"approach":[72],"it":[73],"as":[74,84],"sequence":[76],"learning":[77],"with":[79],"powerful":[80],"neural":[81,86,111],"networks":[82,87],"such":[83],"recurrent":[85],"and":[88,121],"contextualized":[89],"word":[90],"representation":[91],"models":[92],"trained":[93],"obtain":[95],"semantic":[96,115],"representations":[97],"expressions.":[101],"Our":[102],"experimental":[103],"evaluation":[104],"over":[105],"three":[106],"benchmarks":[108],"shows":[109],"that":[110],"architectures":[112],"leverage":[113],"meaning":[116],"significantly":[122],"outperform":[123],"an":[124],"existing":[125],"state":[126],"art":[129],"models.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2019-06-27T00:00:00"}