{"id":"https://openalex.org/W4416252116","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227659","title":"REIA: Entity Relation Extraction Based on Interaction Policy and Data Augmentation","display_name":"REIA: Entity Relation Extraction Based on Interaction Policy and Data Augmentation","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252116","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227659"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227659","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227659","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5072516394","display_name":"Liruizhi Jia","orcid":"https://orcid.org/0000-0002-0178-4375"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liruizhi Jia","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025798843","display_name":"Shengquan Liu","orcid":"https://orcid.org/0000-0001-9623-4714"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengquan Liu","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033203778","display_name":"Bo Kong","orcid":"https://orcid.org/0000-0002-6002-9150"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Kong","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100596622","display_name":"Yuan Liu","orcid":"https://orcid.org/0000-0002-1361-0882"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Liu","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5072516394"],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19511949,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"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":0.8176000118255615,"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":0.8176000118255615,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.050999999046325684,"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":0.01889999955892563,"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":0.8029999732971191},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6195999979972839},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.52920001745224},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5213000178337097},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4876999855041504},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4787999987602234},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.4636000096797943},{"id":"https://openalex.org/keywords/semantic-relation","display_name":"Semantic relation","score":0.45500001311302185}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.807699978351593},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.8029999732971191},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6195999979972839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5792999863624573},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5601000189781189},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5213000178337097},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.4636000096797943},{"id":"https://openalex.org/C2988080768","wikidata":"https://www.wikidata.org/wiki/Q7095057","display_name":"Semantic relation","level":3,"score":0.45500001311302185},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C69075417","wikidata":"https://www.wikidata.org/wiki/Q515701","display_name":"Linked data","level":3,"score":0.4081000089645386},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C31266012","wikidata":"https://www.wikidata.org/wiki/Q6554340","display_name":"Linkage (software)","level":3,"score":0.3659000098705292},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3517000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C2778780117","wikidata":"https://www.wikidata.org/wiki/Q3269423","display_name":"Reading comprehension","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.2928999960422516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2816999852657318},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227659","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227659","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1551842868","https://openalex.org/W1902237438","https://openalex.org/W2250999640","https://openalex.org/W2513378248","https://openalex.org/W2515462165","https://openalex.org/W2587809655","https://openalex.org/W2600659824","https://openalex.org/W2606974598","https://openalex.org/W2798734500","https://openalex.org/W2949922292","https://openalex.org/W2951231735","https://openalex.org/W2953356739","https://openalex.org/W2962881743","https://openalex.org/W2963545917","https://openalex.org/W2963718112","https://openalex.org/W2964167098","https://openalex.org/W2964206023","https://openalex.org/W2964217331","https://openalex.org/W2964273534","https://openalex.org/W2970120757","https://openalex.org/W2970501962","https://openalex.org/W2979400990","https://openalex.org/W2983995706","https://openalex.org/W3034617555","https://openalex.org/W3034797437","https://openalex.org/W3034862440","https://openalex.org/W3128829605","https://openalex.org/W3156977337","https://openalex.org/W4220958359","https://openalex.org/W4285305471","https://openalex.org/W4289878414","https://openalex.org/W4327571437","https://openalex.org/W4376619305","https://openalex.org/W4380591192","https://openalex.org/W4382202654","https://openalex.org/W4385245566","https://openalex.org/W4385570512","https://openalex.org/W4385572187"],"related_works":[],"abstract_inverted_index":{"Extracting":[0],"entities":[1],"and":[2,48,58,64,78,100,107,122,130],"relations":[3],"from":[4],"unstructured":[5],"text":[6,26],"is":[7],"an":[8,89],"important":[9],"task":[10,32],"in":[11,22],"information":[12],"extraction.":[13],"In":[14],"recent":[15],"years,":[16],"great":[17],"success":[18],"has":[19],"been":[20],"achieved":[21],"helping":[23],"models":[24],"understand":[25],"by":[27,120],"formalizing":[28],"the":[29,95,105,116],"relation":[30,49],"extraction":[31],"as":[33],"a":[34],"machine":[35],"reading":[36],"comprehension":[37],"problem.":[38],"However,":[39],"existing":[40],"approaches":[41],"often":[42],"lack":[43],"semantic":[44,60,96],"understanding":[45],"of":[46,55,128],"entity":[47],"categories,":[50],"leading":[51],"to":[52,93],"poor":[53],"differentiation":[54],"ambiguous":[56],"types":[57],"weak":[59],"linkage":[61],"between":[62,98],"queries":[63,99],"context.":[65,101],"To":[66],"address":[67],"these":[68],"problems,":[69],"our":[70,113],"research":[71],"infuses":[72],"attribute":[73],"knowledge":[74],"into":[75],"query":[76],"representations":[77],"enhances":[79],"model":[80,114],"robustness":[81],"through":[82],"adversarial":[83],"data":[84],"augmentation.":[85],"Furthermore,":[86],"we":[87],"introduce":[88],"interactive":[90],"attention":[91],"mechanism":[92],"deepen":[94],"analysis":[97],"Our":[102],"experiments":[103],"on":[104],"ACE2004":[106],"ACE2005":[108],"datasets":[109],"demonstrate":[110],"significant":[111],"improvements;":[112],"outperforms":[115],"baseline":[117],"F1":[118,126],"scores":[119,127],"11.1%":[121],"3.5%,":[123],"respectively,":[124],"achieving":[125],"60.5%":[129],"63.7%.":[131]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}