{"id":"https://openalex.org/W4416251726","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228156","title":"Guidelines-Enhanced ICL with Examples Selection for Document-Level Event Argument Extraction","display_name":"Guidelines-Enhanced ICL with Examples Selection for Document-Level Event Argument Extraction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251726","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228156"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228156","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":"conference-paper","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/A5101132298","display_name":"Yuning Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuning Guo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013602473","display_name":"Xuanning Liu","orcid":"https://orcid.org/0009-0000-3130-5139"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuanning Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007360737","display_name":"Bin Wu","orcid":"https://orcid.org/0000-0002-4743-1006"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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":0.8263999819755554,"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":0.8263999819755554,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.021199999377131462,"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":0.02019999921321869,"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/event","display_name":"Event (particle physics)","score":0.675599992275238},{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.5889999866485596},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.585099995136261},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5662000179290771},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45350000262260437},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3774000108242035},{"id":"https://openalex.org/keywords/complex-event-processing","display_name":"Complex event processing","score":0.3605000078678131},{"id":"https://openalex.org/keywords/fragment","display_name":"Fragment (logic)","score":0.34470000863075256}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775999903678894},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.675599992275238},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.5889999866485596},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.585099995136261},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5662000179290771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4986000061035156},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45350000262260437},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39800000190734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3905999958515167},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.3605000078678131},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3513999879360199},{"id":"https://openalex.org/C2776235265","wikidata":"https://www.wikidata.org/wiki/Q18392052","display_name":"Fragment (logic)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228156","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":27,"referenced_works":["https://openalex.org/W2138779671","https://openalex.org/W3034900014","https://openalex.org/W3102925419","https://openalex.org/W3104597568","https://openalex.org/W3122241445","https://openalex.org/W3161207330","https://openalex.org/W3170759063","https://openalex.org/W3176453404","https://openalex.org/W4221159406","https://openalex.org/W4225321042","https://openalex.org/W4225373045","https://openalex.org/W4287891464","https://openalex.org/W4378906481","https://openalex.org/W4385565231","https://openalex.org/W4385571085","https://openalex.org/W4385571184","https://openalex.org/W4385571940","https://openalex.org/W4385571963","https://openalex.org/W4385573087","https://openalex.org/W4387848718","https://openalex.org/W4401043290","https://openalex.org/W4401626333","https://openalex.org/W4402670546","https://openalex.org/W4402671544","https://openalex.org/W4402672030","https://openalex.org/W4402672106","https://openalex.org/W4404782964"],"related_works":[],"abstract_inverted_index":{"Document-level":[0],"event":[1,12],"argument":[2],"extraction":[3],"(DEAE)":[4],"aims":[5],"to":[6,88,101],"structure":[7],"unstructured":[8],"documents":[9],"by":[10],"identifying":[11],"participants,":[13],"supporting":[14],"various":[15],"downstream":[16],"tasks.":[17],"Traditional":[18],"methods":[19],"rely":[20],"on":[21],"fine-tuning":[22],"pre-trained":[23],"models,":[24],"demanding":[25],"substantial":[26],"data":[27],"and":[28,53,91,114],"complex":[29],"architectures.":[30],"In-context":[31],"learning":[32],"(ICL)":[33],"provides":[34],"a":[35,77,83,97],"promising":[36],"solution.":[37],"However,":[38],"its":[39,133],"application":[40],"in":[41,124],"DEAE":[42],"tasks":[43],"faces":[44],"two":[45],"challenges:":[46],"(1)":[47],"inadequate":[48],"strategies":[49],"for":[50],"selecting":[51],"examples":[52,113],"(2)":[54],"insufficient":[55],"integration":[56],"of":[57,132],"error":[58,104],"feedback":[59,105],"from":[60],"large":[61],"language":[62],"models":[63],"(LLMs).":[64],"To":[65],"address":[66],"these":[67],"challenges,":[68],"we":[69,110],"introduce":[70],"GEIES":[71,95],"(Guidelines-Enhanced":[72],"ICL":[73,79],"with":[74,126],"Examples":[75],"Selection),":[76],"novel":[78],"framework.":[80],"It":[81],"uses":[82],"dual":[84],"similarity":[85],"selector":[86],"(DSS)":[87],"select":[89],"diverse":[90],"relevant":[92],"examples.":[93],"Additionally,":[94],"employs":[96],"guidelines":[98,115],"generator":[99],"(GG)":[100],"convert":[102],"LLM":[103],"into":[106,116],"actionable":[107],"guidelines.":[108],"Finally,":[109],"embed":[111],"the":[112,117,130],"prompt":[118],"context.":[119],"Experiments":[120],"confirm":[121],"GEIES\u2019s":[122],"effectiveness":[123],"DEAE,":[125],"ablation":[127],"studies":[128],"validating":[129],"contributions":[131],"components.":[134]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}