{"id":"https://openalex.org/W2978100584","doi":"https://doi.org/10.1109/ijcnn.2019.8852378","title":"A Distant Supervised Relation Extraction Model with Two Denoising Strategies","display_name":"A Distant Supervised Relation Extraction Model with Two Denoising Strategies","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978100584","doi":"https://doi.org/10.1109/ijcnn.2019.8852378","mag":"2978100584"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5041513266","display_name":"Zikai Zhou","orcid":"https://orcid.org/0000-0003-1872-1386"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zikai Zhou","raw_affiliation_strings":["School of Software Engineering, South China University of Technology, Guangzhou"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, South China University of Technology, Guangzhou","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102008984","display_name":"Yi Cai","orcid":"https://orcid.org/0000-0002-9798-2463"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Cai","raw_affiliation_strings":["School of Software Engineering, South China University of Technology, Guangzhou"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, South China University of Technology, Guangzhou","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086937566","display_name":"Jingyun Xu","orcid":"https://orcid.org/0000-0001-5511-6826"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyun Xu","raw_affiliation_strings":["School of Software Engineering, South China University of Technology, Guangzhou"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, South China University of Technology, Guangzhou","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049131355","display_name":"Jiayuan Xie","orcid":"https://orcid.org/0000-0002-6833-7879"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayuan Xie","raw_affiliation_strings":["School of Automation, Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404176","display_name":"Qing Li","orcid":"https://orcid.org/0000-0003-3370-471X"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Qing Li","raw_affiliation_strings":["Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR"],"affiliations":[{"raw_affiliation_string":"Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013151488","display_name":"Haoran Xie","orcid":"https://orcid.org/0000-0003-0965-3617"},"institutions":[{"id":"https://openalex.org/I4210086892","display_name":"Education University of Hong Kong","ror":"https://ror.org/000t0f062","country_code":"HK","type":"education","lineage":["https://openalex.org/I4210086892"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Haoran Xie","raw_affiliation_strings":["Department of MIT, The Education University of Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of MIT, The Education University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I4210086892"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5041513266"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.1160332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"333","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9994000196456909,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9983999729156494,"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":0.7652332782745361},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6467846632003784},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6380085945129395},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6230559349060059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6130286455154419},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5767198204994202},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5505700707435608},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5249711871147156},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.4643920063972473},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.462897926568985},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45249244570732117},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4516947269439697},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.2208469808101654},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1997886300086975},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19722074270248413},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17045363783836365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7652332782745361},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6467846632003784},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6380085945129395},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6230559349060059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6130286455154419},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5767198204994202},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5505700707435608},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5249711871147156},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.4643920063972473},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.462897926568985},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45249244570732117},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4516947269439697},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2208469808101654},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1997886300086975},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19722074270248413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17045363783836365},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W174427690","https://openalex.org/W1604644367","https://openalex.org/W2072128103","https://openalex.org/W2095705004","https://openalex.org/W2107598941","https://openalex.org/W2123442489","https://openalex.org/W2132679783","https://openalex.org/W2155454737","https://openalex.org/W2157331557","https://openalex.org/W2158899491","https://openalex.org/W2250521169","https://openalex.org/W2251135946","https://openalex.org/W2265846598","https://openalex.org/W2515462165","https://openalex.org/W2517194566","https://openalex.org/W2572908757","https://openalex.org/W2577666659","https://openalex.org/W2604610161","https://openalex.org/W2751669643","https://openalex.org/W2760600531","https://openalex.org/W2806860662","https://openalex.org/W2952230511","https://openalex.org/W2964317478","https://openalex.org/W4231109964","https://openalex.org/W4294170691","https://openalex.org/W6607091552","https://openalex.org/W6674330103","https://openalex.org/W6679781796","https://openalex.org/W6682691769","https://openalex.org/W6683738474","https://openalex.org/W6691723933","https://openalex.org/W6693505360","https://openalex.org/W6732352985","https://openalex.org/W6735888106","https://openalex.org/W6743817965"],"related_works":["https://openalex.org/W4387688064","https://openalex.org/W4390516098","https://openalex.org/W2976808399","https://openalex.org/W2181948922","https://openalex.org/W2375873920","https://openalex.org/W2384362569","https://openalex.org/W2146114872","https://openalex.org/W2142795561","https://openalex.org/W2805262146","https://openalex.org/W4379517534"],"abstract_inverted_index":{"Distant":[0],"supervised":[1,17],"relation":[2,32,180],"extraction":[3,33],"has":[4,59],"been":[5],"an":[6,47],"effective":[7,48],"way":[8,49],"to":[9,28,50,100,135,192],"find":[10],"relational":[11],"facts":[12],"from":[13,197],"text.":[14],"However,":[15],"distant":[16],"method":[18],"inevitably":[19],"accompanies":[20],"with":[21,42],"wrongly":[22,162],"labeled":[23,201],"sentences.":[24,104],"Noisy":[25],"sentences":[26],"lead":[27],"poor":[29],"performance":[30],"of":[31,54,79,103,131,199,214,240],"models.":[34],"Though":[35],"existing":[36],"piecewise":[37],"convolutional":[38],"neural":[39],"network":[40],"model":[41,124,145,225],"sentence-level":[43,149],"attention":[44,150],"(PCNN+ATT)":[45],"is":[46],"reduce":[51,193],"the":[52,87,92,101,120,141,161,168,173,179,212,223,230],"effect":[53],"noisy":[55],"sentences,":[56],"it":[57,65,90],"still":[58],"two":[60,108,187,231],"limitations.":[61],"On":[62,86],"one":[63],"hand,":[64,89],"adopts":[66],"a":[67,112,126],"PCNN":[68],"module":[69,128],"as":[70,134],"sentence":[71,156],"encoder,":[72],"which":[73,152],"only":[74],"captures":[75],"local":[76],"contextual":[77,138],"features":[78],"words":[80,97],"and":[81,148,178,203,216,229],"might":[82],"lose":[83],"important":[84],"information.":[85,139],"other":[88],"neglects":[91],"fact":[93],"that":[94,222],"not":[95],"all":[96],"contribute":[98],"equally":[99],"semantics":[102],"To":[105,158],"address":[106],"these":[107,184],"issues,":[109],"we":[110,165],"propose":[111],"hierarchical":[113],"attention-based":[114],"bidirectional":[115],"GRU":[116],"(HA-BiGRU)":[117],"model.":[118,208,242],"For":[119,140],"first":[121,166],"limitation,":[122,143],"our":[123,144],"utilizes":[125],"BiGRU":[127],"in":[129],"place":[130],"PCNN,":[132],"so":[133],"extract":[136],"global":[137],"second":[142],"combines":[146],"word-level":[147],"mechanisms,":[151],"help":[153],"get":[154],"accurate":[155],"representations.":[157],"further":[159],"alleviate":[160],"labeling":[163],"problem,":[164],"calculate":[167],"co-occurrence":[169,185,232],"probabilities":[170,233],"(CP)":[171],"between":[172],"shortest":[174],"dependency":[175],"path":[176],"(SDP)":[177],"labels.":[181],"Based":[182],"on":[183,211],"probabilities,":[186],"denoising":[188,235],"strategies":[189,236],"are":[190],"proposed":[191],"noise":[194],"interference":[195],"respectively":[196],"aspect":[198],"filtering":[200],"data":[202],"integrating":[204],"CP":[205],"information":[206],"into":[207],"Experimental":[209],"results":[210],"corpus":[213],"Freebase":[215],"New":[217],"York":[218],"Times":[219],"(Freebase+NYT)":[220],"show":[221],"HA-BiGRU":[224,241],"outperforms":[226],"baseline":[227],"models,":[228],"based":[234],"can":[237],"improve":[238],"robustness":[239]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
