{"id":"https://openalex.org/W4200280991","doi":"https://doi.org/10.1145/3500931.3500939","title":"Entity Pair Recognition using Semantic Enrichment and Adversarial Training for Chinese Drug Knowledge Extraction","display_name":"Entity Pair Recognition using Semantic Enrichment and Adversarial Training for Chinese Drug Knowledge Extraction","publication_year":2021,"publication_date":"2021-10-29","ids":{"openalex":"https://openalex.org/W4200280991","doi":"https://doi.org/10.1145/3500931.3500939"},"language":"en","primary_location":{"id":"doi:10.1145/3500931.3500939","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3500931.3500939","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049722352","display_name":"Feng Gao","orcid":"https://orcid.org/0000-0002-2396-1360"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Gao","raw_affiliation_strings":["Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029991332","display_name":"Lunsheng Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"LunSheng Zhou","raw_affiliation_strings":["Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007757643","display_name":"Jinguang Gu","orcid":"https://orcid.org/0000-0002-8823-8480"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JinGuang Gu","raw_affiliation_strings":["Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I43922553"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049722352"],"corresponding_institution_ids":["https://openalex.org/I43922553"],"apc_list":null,"apc_paid":null,"fwci":0.2719,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64934115,"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":"35","last_page":"42"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9966999888420105,"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":0.9966999888420105,"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":0.984499990940094,"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"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9653000235557556,"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":0.8179174661636353},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.663007378578186},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6538512110710144},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.603019654750824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5160977244377136},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4955955743789673},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.44824087619781494},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4119030237197876},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.40997424721717834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3322475552558899},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08438614010810852}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8179174661636353},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.663007378578186},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6538512110710144},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.603019654750824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5160977244377136},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4955955743789673},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.44824087619781494},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4119030237197876},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40997424721717834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3322475552558899},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08438614010810852},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3500931.3500939","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3500931.3500939","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G2338979300","display_name":null,"funder_award_id":"U1836118","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2122377078","https://openalex.org/W2194775991","https://openalex.org/W3013842359","https://openalex.org/W3120867461","https://openalex.org/W3162462834","https://openalex.org/W3173783447"],"related_works":["https://openalex.org/W2805262146","https://openalex.org/W4379517534","https://openalex.org/W842810586","https://openalex.org/W4319940250","https://openalex.org/W2352298027","https://openalex.org/W2092919065","https://openalex.org/W3138801416","https://openalex.org/W4236762297","https://openalex.org/W2444550338","https://openalex.org/W2369351710"],"abstract_inverted_index":{"Existing":[0],"knowledge":[1,74,102],"extraction":[2,72],"methods":[3,45],"in":[4,35,64,80],"pharmacy":[5],"often":[6],"use":[7],"natural":[8],"language":[9],"processing":[10],"tools":[11],"and":[12,20,43,73,122],"deep":[13],"learning":[14],"model":[15,116,147],"to":[16,101,154,159],"identify":[17],"drug":[18,25,36,134],"entities":[19],"extract":[21],"their":[22],"relationships":[23],"from":[24],"instructions,":[26],"thus":[27],"obtaining":[28],"drug-drug":[29],"or":[30,54],"drug-disease":[31],"knowledge.":[32],"However,":[33],"sentences":[34],"instructions":[37,135],"may":[38],"contain":[39],"multiple":[40],"drug-related":[41],"entities,":[42],"existing":[44,164],"lack":[46],"the":[47,52,65,81,126,137,142,146,163],"capability":[48],"of":[49,133,145],"identifying":[50],"valid":[51,93],"\"drug-drug\"":[53],"\"drug-disease\"":[55],"entity":[56,70,94,113,119,149],"pairs.":[57],"This":[58],"will":[59],"introduce":[60],"significant":[61],"noise":[62],"data":[63],"subsequent":[66],"tasks":[67],"such":[68,96],"as":[69],"relationship":[71],"graph":[75],"construction.":[76],"Meanwhile,":[77],"some":[78],"mentions":[79],"sentence":[82],"can":[83],"have":[84],"hierarchical":[85],"relations":[86],"even":[87],"if":[88],"they":[89],"do":[90],"not":[91],"form":[92],"pairs,":[95],"information":[97],"is":[98,152,157],"also":[99],"crucial":[100],"extraction.":[103],"To":[104],"solve":[105],"these":[106],"two":[107],"problems,":[108],"this":[109],"paper":[110],"proposes":[111],"an":[112],"pair":[114,150],"verification":[115,151],"based":[117],"on":[118,128],"semantic":[120],"enhancement":[121],"adversarial":[123],"training.":[124],"Through":[125],"experiment":[127],"more":[129],"than":[130],"2000":[131],"kinds":[132],"data,":[136],"experimental":[138],"results":[139],"show":[140],"that":[141],"F1":[143],"value":[144],"for":[148],"up":[153,158],"98.65%,":[155],"which":[156],"9.37%":[160],"compared":[161],"with":[162],"methods.":[165]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}