{"id":"https://openalex.org/W4416034940","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.419","title":"Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation","display_name":"Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034940","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.419"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.419","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.419","pdf_url":"https://aclanthology.org/2025.findings-emnlp.419.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.419.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101776764","display_name":"Sheng Liang","orcid":"https://orcid.org/0009-0008-8460-1749"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100751609","display_name":"Hang Lv","orcid":"https://orcid.org/0000-0003-3739-443X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang Lv","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088110992","display_name":"Zhihao Wen","orcid":"https://orcid.org/0000-0002-7688-5381"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhihao Wen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102858602","display_name":"Yaxiong Wu","orcid":"https://orcid.org/0000-0002-1860-0122"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yaxiong Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100312223","display_name":"Yongyue Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongyue Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446020","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0001-5047-9978"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072988055","display_name":"Yong Liu","orcid":"https://orcid.org/0000-0001-9031-9696"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yong Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7588,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.89409288,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"7927","last_page":"7946"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10906","display_name":"AI-based Problem Solving and Planning","score":0.22579999268054962,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.22579999268054962,"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/T13062","display_name":"Cognitive Computing and Networks","score":0.07569999992847443,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.061500001698732376,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.4902999997138977},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2847999930381775},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.28189998865127563},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.2791000008583069},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.26179999113082886}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5449000000953674},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.444599986076355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26179999113082886},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2533999979496002},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.241799995303154}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.419","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.419","pdf_url":"https://aclanthology.org/2025.findings-emnlp.419.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.419","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.419","pdf_url":"https://aclanthology.org/2025.findings-emnlp.419.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034940.pdf","grobid_xml":"https://content.openalex.org/works/W4416034940.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Event":[0,99,130],"extraction":[1,40],"(EE)":[2],"is":[3],"a":[4,102],"fundamental":[5],"task":[6],"in":[7,23,52],"natural":[8],"language":[9,70,145],"processing":[10],"(NLP)":[11],"that":[12,151],"involves":[13],"identifying":[14],"and":[15,37,56,67,80,116,144,169],"extracting":[16],"event":[17,170],"information":[18],"from":[19,33],"unstructured":[20],"text.Effective":[21],"EE":[22],"real-world":[24],"scenarios":[25],"requires":[26],"two":[27,44],"key":[28],"steps:":[29],"selecting":[30],"appropriate":[31],"schemas":[32,108],"hundreds":[34],"of":[35,60,84,165],"candidates":[36],"executing":[38],"the":[39,48,58,81,127,163],"process.Existing":[41],"research":[42],"exhibits":[43],"critical":[45],"gaps:":[46],"(1)":[47],"rigid":[49],"schema":[50,65,78,88,110,167],"fixation":[51],"existing":[53],"pipeline":[54],"systems,":[55],"(2)":[57],"absence":[59],"benchmarks":[61],"for":[62,87],"evaluating":[63],"joint":[64,166],"matching":[66],"extraction.While":[68],"large":[69],"models":[71],"(LLMs)":[72],"show":[73,150],"promise,":[74],"challenges":[75],"such":[76],"as":[77],"hallucination":[79],"inefficient":[82],"use":[83],"long":[85],"contexts":[86],"selection":[89],"hinder":[90],"their":[91],"practical":[92],"deployment.In":[93],"response,":[94],"we":[95,125],"propose":[96],"Adaptive":[97],"Schemaaware":[98],"Extraction":[100,131],"(ASEE),":[101],"retrievalaugmented":[103],"generation":[104],"paradigm.ASEE":[105],"first":[106],"enriches":[107],"via":[109],"paraphrasing":[111],"to":[112],"improve":[113],"retrieval":[114,168],"accuracy":[115],"then":[117],"performs":[118],"targeted,":[119],"schema-guided":[120],"extraction.To":[121],"facilitate":[122],"rigorous":[123],"evaluation,":[124],"construct":[126],"Multi-Dimensional":[128],"Schema-aware":[129],"(MD-SEE)":[132],"benchmark,":[133],"which":[134],"systematically":[135],"consolidates":[136],"12":[137],"datasets":[138],"across":[139,158],"diverse":[140],"domains,":[141],"complexity":[142],"levels,":[143],"settings.Extensive":[146],"evaluations":[147],"on":[148],"MD-SEE":[149],"our":[152],"proposed":[153],"ASEE":[154],"demonstrates":[155],"strong":[156],"adaptability":[157],"various":[159],"scenarios,":[160],"significantly":[161],"improving":[162],"performance":[164],"extraction.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}