{"id":"https://openalex.org/W4392254150","doi":"https://doi.org/10.1145/3639479.3639513","title":"Entity relation extraction based on Biaffine model with embedded context features","display_name":"Entity relation extraction based on Biaffine model with embedded context features","publication_year":2023,"publication_date":"2023-12-27","ids":{"openalex":"https://openalex.org/W4392254150","doi":"https://doi.org/10.1145/3639479.3639513"},"language":"en","primary_location":{"id":"doi:10.1145/3639479.3639513","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3639479.3639513","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3639479.3639513","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3639479.3639513","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113129711","display_name":"Suwei Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suwei Wu","raw_affiliation_strings":["Shandong University of Science and Technology, China"],"raw_orcid":"https://orcid.org/0009-0006-3422-4864","affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034549860","display_name":"Fan Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Xiao","raw_affiliation_strings":["Shandong University of Science and Technology, China"],"raw_orcid":"https://orcid.org/0009-0001-9885-0868","affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037310126","display_name":"Bing Gu","orcid":"https://orcid.org/0009-0005-8967-2026"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Gu","raw_affiliation_strings":["Shandong University of Science and Technology, China"],"raw_orcid":"https://orcid.org/0009-0005-8967-2026","affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101482521","display_name":"Xin Eric Wang","orcid":"https://orcid.org/0009-0009-2080-1917"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Shandong University of Science and Technology, China"],"raw_orcid":"https://orcid.org/0009-0009-2080-1917","affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108318108","display_name":"Qingge Fang","orcid":"https://orcid.org/0009-0003-5799-1888"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingge Fang","raw_affiliation_strings":["Shandong University of Science and Technology, China"],"raw_orcid":"https://orcid.org/0009-0003-5799-1888","affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101558032","display_name":"Changqing Yan","orcid":"https://orcid.org/0000-0002-0571-8025"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changqing Yan","raw_affiliation_strings":["Shandong University of Science and Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-0571-8025","affiliations":[{"raw_affiliation_string":"Shandong University of Science and Technology, China","institution_ids":["https://openalex.org/I80143920"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2481156,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"161","last_page":"165"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10028","display_name":"Topic Modeling","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/T11719","display_name":"Data Quality and Management","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9955999851226807,"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/relationship-extraction","display_name":"Relationship extraction","score":0.8879266381263733},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7763189077377319},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6883833408355713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5876140594482422},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.49398475885391235},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4814012050628662},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46628472208976746},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.45158112049102783},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.44708213210105896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4432436227798462},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4320194125175476},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.42698973417282104},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4218325614929199},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39761513471603394},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3476826548576355}],"concepts":[{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.8879266381263733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7763189077377319},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6883833408355713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5876140594482422},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.49398475885391235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4814012050628662},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46628472208976746},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.45158112049102783},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.44708213210105896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4432436227798462},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4320194125175476},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.42698973417282104},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4218325614929199},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39761513471603394},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3476826548576355},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3639479.3639513","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3639479.3639513","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3639479.3639513","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3639479.3639513","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3639479.3639513","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3639479.3639513","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392254150.pdf","grobid_xml":"https://content.openalex.org/works/W4392254150.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2250539671","https://openalex.org/W2798734500","https://openalex.org/W2949204161","https://openalex.org/W2964217331","https://openalex.org/W3173628900","https://openalex.org/W6603527449"],"related_works":["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","https://openalex.org/W2594363579","https://openalex.org/W2169232658"],"abstract_inverted_index":{"The":[0],"extraction":[1,32,57,65,69,145],"of":[2,23,58,63,70,114,137,228,261],"entities":[3],"and":[4,37,43,60,72,104,127,230,242,251],"relationships":[5,206],"from":[6,140],"unstructured":[7],"text":[8],"is":[9,52,217],"not":[10],"only":[11,79],"a":[12,185,219],"critical":[13],"issue":[14],"in":[15,88,146],"information":[16],"extraction,":[17],"but":[18],"also":[19],"an":[20,199],"essential":[21],"component":[22],"constructing":[24],"knowledge":[25],"graphs.":[26],"Most":[27],"existing":[28,182],"models":[29,78,183],"for":[30,91,124,188,207,221],"relation":[31,75,92,128],"first":[33],"extract":[34,41],"all":[35,204,269],"subjects,":[36,59],"then":[38],"proceed":[39],"to":[40,97,155,166,176,202],"objects":[42,71],"relations":[44],"based":[45],"on":[46,55,248],"the":[47,56,61,68,81,85,89,98,102,105,111,130,141,168,172,177,194,226,238,245,249,258,262],"identified":[48],"subjects.":[49],"This":[50,163],"method":[51],"highly":[53],"dependent":[54],"quality":[62],"subject":[64,103,175],"significantly":[66],"impacts":[67],"relations.":[73],"During":[74],"processing,":[76,93],"most":[77,181],"use":[80,184],"simplest":[82],"classifier,":[83],"with":[84,198],"difference":[86],"lying":[87],"input":[90,115],"which":[94],"sometimes":[95],"leads":[96],"mutual":[99],"relationship":[100,173],"between":[101,240],"object":[106],"being":[107,174],"overlooked.To":[108],"avoid":[109],"overlooking":[110],"inherent":[112],"features":[113,139,227],"sentences,":[116],"we":[117],"generate":[118],"three":[119],"distinct":[120],"token":[121],"representation":[122],"sequences":[123],"subject,":[125],"object,":[126],"at":[129],"encoding":[131,142],"layer.":[132],"Additionally,":[133],"combining":[134],"explicit":[135],"injection":[136],"context":[138],"layer,":[143],"entity":[144,159,209],"both":[147],"directions":[148],"can":[149,235],"mutually":[150],"promote":[151],"each":[152,208,222],"other,":[153],"aiming":[154],"obtain":[156],"as":[157,161],"many":[158],"pairs":[160],"possible.":[162],"approach":[164],"aims":[165],"address":[167],"limitations":[169],"imposed":[170],"by":[171],"extracted":[178],"subject.":[179],"While":[180],"simple":[186,215],"classifier":[187],"handling":[189],"relationships,":[190],"our":[191],"model":[192,196,247],"employs":[193],"Biaffine":[195],"combined":[197],"attention":[200],"mechanism":[201,234],"assign":[203],"potential":[205],"pair.":[210],"Its":[211],"advantage":[212],"over":[213],"other":[214],"classifiers":[216],"maintaining":[218],"matrix":[220],"relation,":[223],"accurately":[224],"modeling":[225],"relations,":[229],"its":[231],"probability":[232],"calculation":[233],"precisely":[236],"explore":[237],"interaction":[239],"subjects":[241],"objects.We":[243],"evaluated":[244],"proposed":[246,263],"NYT":[250],"WebNLG":[252],"datasets.":[253,270],"Extensive":[254],"experimental":[255],"results":[256],"demonstrate":[257],"high":[259],"effectiveness":[260],"model,":[264],"achieving":[265],"significant":[266],"performance":[267],"across":[268]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}