{"id":"https://openalex.org/W3175116650","doi":"https://doi.org/10.1145/3457682.3457765","title":"GCN2-NAA: Two-stage Graph Convolutional Networks with Node-Aware Attention for Joint Entity and Relation Extraction","display_name":"GCN2-NAA: Two-stage Graph Convolutional Networks with Node-Aware Attention for Joint Entity and Relation Extraction","publication_year":2021,"publication_date":"2021-02-26","ids":{"openalex":"https://openalex.org/W3175116650","doi":"https://doi.org/10.1145/3457682.3457765","mag":"3175116650"},"language":"en","primary_location":{"id":"doi:10.1145/3457682.3457765","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3457682.3457765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Machine Learning and Computing","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/A5010853302","display_name":"Weicai Niu","orcid":null},"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":true,"raw_author_name":"WeiCai Niu","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100377856","display_name":"Quan Chen","orcid":"https://orcid.org/0000-0003-2034-0371"},"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":"Quan Chen","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101643168","display_name":"Weiwen Zhang","orcid":"https://orcid.org/0000-0002-5098-6459"},"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":"Weiwen Zhang","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053030022","display_name":"Jianwen Ma","orcid":"https://orcid.org/0000-0003-3470-2636"},"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":"Jianwen Ma","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066449239","display_name":"Zhongqiang Hu","orcid":null},"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":"Zhongqiang Hu","raw_affiliation_strings":["Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5010853302"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.6798,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75290344,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"542","last_page":"549"},"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":0.9994999766349792,"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":0.994700014591217,"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":0.8178806304931641},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7210568189620972},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6089268326759338},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5746369957923889},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.542713463306427},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5140050649642944},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5064109563827515},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.503513514995575},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4885922074317932},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4459958076477051},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.43852078914642334},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.43280327320098877},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.4273512065410614},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.41445302963256836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41418343782424927},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3664374053478241},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2872591018676758},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.2217096984386444},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09872916340827942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8178806304931641},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7210568189620972},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6089268326759338},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5746369957923889},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.542713463306427},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5140050649642944},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5064109563827515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.503513514995575},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4885922074317932},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4459958076477051},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.43852078914642334},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.43280327320098877},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.4273512065410614},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.41445302963256836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41418343782424927},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3664374053478241},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2872591018676758},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2217096984386444},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09872916340827942},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3457682.3457765","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3457682.3457765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7400000095367432,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G7671022659","display_name":null,"funder_award_id":"62002071","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1604644367","https://openalex.org/W2053238041","https://openalex.org/W2133439966","https://openalex.org/W2134033474","https://openalex.org/W2250521169","https://openalex.org/W2251091211","https://openalex.org/W2517194566","https://openalex.org/W2539469848","https://openalex.org/W2603258515","https://openalex.org/W2694849690","https://openalex.org/W2741956709","https://openalex.org/W2792839191","https://openalex.org/W2798734500","https://openalex.org/W2891935547","https://openalex.org/W2899996857","https://openalex.org/W2905462022","https://openalex.org/W2908230750","https://openalex.org/W2949212908","https://openalex.org/W2951231735","https://openalex.org/W2963625095","https://openalex.org/W2970183140","https://openalex.org/W3007535931","https://openalex.org/W3011704886","https://openalex.org/W3081266640","https://openalex.org/W3098087397","https://openalex.org/W3105557179","https://openalex.org/W6843761164"],"related_works":["https://openalex.org/W2035329725","https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W4376641153","https://openalex.org/W2378857091","https://openalex.org/W4256502920","https://openalex.org/W103652678","https://openalex.org/W4226090359","https://openalex.org/W2999756192","https://openalex.org/W4387707503"],"abstract_inverted_index":{"Joint":[0],"extraction":[1,44],"of":[2,11,68,135],"entities":[3],"and":[4,57,65,75,83,119,131,140],"relations":[5],"is":[6,29],"critical":[7],"for":[8],"many":[9],"tasks":[10],"Natural":[12],"Language":[13],"Processing":[14],"(NLP),":[15],"which":[16],"aims":[17],"to":[18,86,111],"extract":[19],"all":[20,94],"triplets":[21],"in":[22,96,133],"the":[23,26,79,88,102,114],"text.":[24],"However,":[25],"huge":[27],"challenge":[28],"that":[30,124],"a":[31,42,50],"sentence":[32],"usually":[33],"contains":[34],"overlapping":[35],"triplets.":[36,120],"In":[37],"this":[38],"paper,":[39],"we":[40,108],"propose":[41],"joint":[43],"framework":[45],"named":[46],"GCN2-NAA":[47,125],"based":[48],"on":[49,101,138],"two-stage":[51],"Graph":[52],"Convolutional":[53],"Neural":[54],"networks":[55],"(GCN)":[56],"Node-Aware":[58],"Attention":[59],"mechanism.":[60],"We":[61],"obtain":[62,113],"multi-granularity":[63],"representations":[64],"regional":[66],"features":[67],"words":[69,95],"by":[70,129],"stacking":[71],"multiple":[72],"feature":[73],"encoders":[74],"1st-phase":[76],"GCN.":[77],"Besides,":[78],"node-aware":[80],"attention":[81,90,105],"mechanism":[82],"2nd-phase":[84],"GCN":[85,110],"capture":[87],"soft":[89,104],"correlation":[91,106],"matrix":[92],"between":[93,116],"each":[97],"relation":[98],"type.":[99],"Based":[100],"constructed":[103],"matrix,":[107],"utilize":[109],"further":[112],"interaction":[115],"entities,":[117],"relations,":[118],"Experiment":[121],"results":[122],"show":[123],"outperforms":[126],"baseline":[127],"models":[128],"6.5%":[130],"11.4%":[132],"terms":[134],"F1":[136],"score":[137],"NYT":[139],"WebNLG":[141],"datasets,":[142],"respectively.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}