{"id":"https://openalex.org/W2771058867","doi":"https://doi.org/10.1109/kse.2017.8119474","title":"Improving chemical-induced disease relation extraction with learned features based on convolutional neural network","display_name":"Improving chemical-induced disease relation extraction with learned features based on convolutional neural network","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2771058867","doi":"https://doi.org/10.1109/kse.2017.8119474","mag":"2771058867"},"language":"en","primary_location":{"id":"doi:10.1109/kse.2017.8119474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kse.2017.8119474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 9th International Conference on Knowledge and Systems Engineering (KSE)","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/A5057642796","display_name":"Hoang-Quynh Le","orcid":"https://orcid.org/0000-0002-1778-0600"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoang-Quynh Le","raw_affiliation_strings":["Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086574827","display_name":"Duy-Cat Can","orcid":"https://orcid.org/0000-0002-6861-2893"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duy-Cat Can","raw_affiliation_strings":["Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078483041","display_name":"Thanh Hai Dang","orcid":"https://orcid.org/0000-0002-0666-9958"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thanh Hai Dang","raw_affiliation_strings":["Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036041371","display_name":"Mai-Vu Tran","orcid":"https://orcid.org/0000-0003-1602-5424"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mai-Vu Tran","raw_affiliation_strings":["Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071516373","display_name":"Quang-Thuy Ha","orcid":"https://orcid.org/0000-0002-3901-3357"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quang-Thuy Ha","raw_affiliation_strings":["Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, University of Engineering and Technology, Hanoi, VNUH, Vietnam","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073413742","display_name":"Nigel Collier","orcid":"https://orcid.org/0000-0002-7230-4164"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nigel Collier","raw_affiliation_strings":["Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8261,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.81407711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"292","last_page":"297"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9909999966621399,"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":0.9909999966621399,"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":0.9710000157356262,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9706000089645386,"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":0.8343797922134399},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.8145244121551514},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8120197057723999},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7120606899261475},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.6909618377685547},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6500043869018555},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6448021531105042},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.626502513885498},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6242877244949341},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5353138446807861},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.4879395663738251},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47390687465667725},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43198102712631226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39486709237098694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8343797922134399},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.8145244121551514},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8120197057723999},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7120606899261475},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.6909618377685547},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6500043869018555},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6448021531105042},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.626502513885498},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6242877244949341},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5353138446807861},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4879395663738251},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47390687465667725},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43198102712631226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39486709237098694},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/kse.2017.8119474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kse.2017.8119474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 9th International Conference on Knowledge and Systems Engineering (KSE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309617","display_name":"National Foundation for Science and Technology Development","ror":"https://ror.org/04rw64z44"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W1904365287","https://openalex.org/W2099188065","https://openalex.org/W2120615054","https://openalex.org/W2136640051","https://openalex.org/W2138627627","https://openalex.org/W2153138487","https://openalex.org/W2162931300","https://openalex.org/W2250521169","https://openalex.org/W2251622960","https://openalex.org/W2291928379","https://openalex.org/W2335791510","https://openalex.org/W2398682428","https://openalex.org/W2511500211","https://openalex.org/W2511964075","https://openalex.org/W2527896214","https://openalex.org/W2572908757","https://openalex.org/W2951682553","https://openalex.org/W2963171262","https://openalex.org/W2964217331","https://openalex.org/W6683965311","https://openalex.org/W6696857621","https://openalex.org/W6728098186"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"There":[0],"have":[1],"been":[2],"an":[3],"increasing":[4],"number":[5],"of":[6,70,118],"various":[7],"machine":[8],"learning-based":[9],"models":[10],"successfully":[11],"proposed":[12,120],"and":[13,83,94],"applied":[14],"for":[15,86,122],"automatic":[16],"chemical-induced":[17],"disease":[18,82],"(CID)":[19],"relation":[20,56,124],"extraction.":[21,125],"They,":[22],"however,":[23],"usually":[24],"require":[25,37],"carefully":[26],"handcrafted":[27,72],"rich":[28],"feature":[29],"sets,":[30],"which":[31],"rely":[32],"on":[33,104],"expert":[34],"knowledge,":[35],"thus":[36],"expensive":[38],"human":[39],"labor":[40],"but":[41],"normally":[42],"still":[43],"cannot":[44],"generalize":[45],"data":[46],"well":[47],"enough.":[48],"In":[49],"this":[50],"paper,":[51],"we":[52],"propose":[53],"a":[54,64,81,84],"CID":[55,89,123],"extraction":[57],"model":[58,121],"that":[59],"learns":[60],"features":[61],"automatically":[62],"through":[63],"Convolutional":[65],"Neural":[66],"Network":[67],"(CNN)":[68],"instead":[69],"traditional":[71],"features.":[73],"We":[74],"exploit":[75],"the":[76,108,116],"shortest":[77],"dependency":[78],"path":[79],"between":[80],"chemical":[85],"identifying":[87],"their":[88,96],"relation.":[90],"Dependency":[91],"relations,":[92],"with":[93],"without":[95],"direction":[97],"information,":[98],"are":[99,112],"further":[100],"investigated.":[101],"Experimental":[102],"results":[103],"benchmark":[105],"datasets":[106],"(namely":[107],"BioCreative":[109],"V":[110],"dataset)":[111],"very":[113],"potential,":[114],"demonstrating":[115],"effectiveness":[117],"our":[119]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}