{"id":"https://openalex.org/W4416252410","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227875","title":"DPEE: A Dual-Prompt Framework for Overlapping and Nested Event Extraction","display_name":"DPEE: A Dual-Prompt Framework for Overlapping and Nested Event Extraction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252410","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227875"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227875","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":null,"display_name":"Xuqiang Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuqiang Yuan","raw_affiliation_strings":["Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101828077","display_name":"Xuhong Liu","orcid":"https://orcid.org/0000-0002-0915-2508"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuhong Liu","raw_affiliation_strings":["Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013105826","display_name":"Ning Li","orcid":"https://orcid.org/0000-0002-1577-6173"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Li","raw_affiliation_strings":["Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002399867","display_name":"Xiulei Liu","orcid":"https://orcid.org/0000-0002-9303-3682"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiulei Liu","raw_affiliation_strings":["Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Information Science and Technology University,Laboratory of Data Science and Information Studies,Beijing,China","institution_ids":["https://openalex.org/I78675632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16625245,"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":0.6837999820709229,"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":0.6837999820709229,"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":0.10379999876022339,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.043699998408555984,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.729200005531311},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.6078000068664551},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6049000024795532},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.41609999537467957},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.34709998965263367},{"id":"https://openalex.org/keywords/data-integration","display_name":"Data integration","score":0.32850000262260437},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.2937999963760376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516999840736389},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.729200005531311},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.6078000068664551},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6049000024795532},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49239999055862427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4228000044822693},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3885999917984009},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31700000166893005},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C103000020","wikidata":"https://www.wikidata.org/wiki/Q1978426","display_name":"Nested set model","level":3,"score":0.27869999408721924},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C33326189","wikidata":"https://www.wikidata.org/wiki/Q17092450","display_name":"Information integration","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26030001044273376},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C2777532361","wikidata":"https://www.wikidata.org/wiki/Q687185","display_name":"Lexicalization","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227875","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2250999640","https://openalex.org/W2736065754","https://openalex.org/W2788525741","https://openalex.org/W2891553865","https://openalex.org/W2952437275","https://openalex.org/W3103519070","https://openalex.org/W3173905592","https://openalex.org/W3173964750","https://openalex.org/W3176951816","https://openalex.org/W3179555038","https://openalex.org/W4287854428","https://openalex.org/W4385569985","https://openalex.org/W4385572217","https://openalex.org/W4386443326","https://openalex.org/W4388979610","https://openalex.org/W4389524213","https://openalex.org/W4401044087","https://openalex.org/W4402683951","https://openalex.org/W4402684024","https://openalex.org/W4403942906","https://openalex.org/W4404239715","https://openalex.org/W4406276943"],"related_works":[],"abstract_inverted_index":{"Event":[0],"extraction":[1,143],"(EE)":[2],"is":[3],"one":[4],"of":[5,98],"the":[6,34,53,58,87,95,99,132],"key":[7],"tasks":[8],"in":[9],"information":[10,73,82,97],"extraction,":[11],"aiming":[12],"to":[13,37,62,92,103],"automatically":[14],"identify":[15],"events":[16],"and":[17,39,57,65,76,113,137],"their":[18],"corresponding":[19],"arguments":[20],"from":[21,74],"text.":[22],"Previous":[23],"EE":[24,134],"work":[25],"has":[26],"primarily":[27],"focused":[28],"on":[29,131],"flat":[30],"events,":[31],"often":[32],"overlooking":[33],"challenges":[35],"related":[36],"overlapping":[38,64,112],"nested":[40,66,114],"events.":[41,67,100],"To":[42],"tackle":[43],"this":[44,104],"issue,":[45],"we":[46],"propose":[47],"a":[48,80,107],"new":[49],"approach":[50,105],"that":[51,140],"combines":[52],"large":[54],"language":[55,60],"models(LLMs)":[56],"small":[59],"models(SLMs)":[61],"address":[63],"The":[68,84],"LLMs":[69],"capture":[70],"rich":[71],"prior":[72],"text":[75],"use":[77,86],"it":[78],"as":[79,106],"local":[81],"prompt.":[83],"SLMs":[85],"global":[88],"event":[89,127,142],"type":[90],"prompt":[91],"better":[93],"understand":[94],"semantic":[96],"We":[101],"refer":[102],"D":[108],"ual-Prompt":[109],"framework":[110],"for":[111],"E":[115,117],"vent":[116],"xtraction":[118],"(DPEE).":[119],"By":[120],"capturing":[121],"dual-prompt":[122],"features,":[123],"DPEE":[124],"effectively":[125],"enhances":[126],"decoding":[128],"capabilities.":[129],"Experiments":[130],"public":[133],"datasets":[135],"FewFC":[136],"FNDEE":[138],"show":[139],"our":[141],"method":[144],"achieves":[145],"significant":[146],"improvements.":[147]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}