{"id":"https://openalex.org/W4416249628","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228575","title":"Cross-Domain Chinese Event Argument Extraction based on Attention\u2013Enhanced Machine Reading Comprehension","display_name":"Cross-Domain Chinese Event Argument Extraction based on Attention\u2013Enhanced Machine Reading Comprehension","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249628","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228575"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228575","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/A5043356121","display_name":"Shihao Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shihao Xu","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113512730","display_name":"Yawei Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yawei Ma","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101955284","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0003-2195-2310"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China","institution_ids":["https://openalex.org/I141962983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043356121"],"corresponding_institution_ids":["https://openalex.org/I141962983"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18197139,"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":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8521000146865845,"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":0.8521000146865845,"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":0.026000000536441803,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.013899999670684338,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.8497999906539917},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6726999878883362},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4702000021934509},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.45660001039505005},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.44110000133514404},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.42660000920295715},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.41659998893737793},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.37770000100135803}],"concepts":[{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.8497999906539917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6960999965667725},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6726999878883362},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6355999708175659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5957000255584717},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.37770000100135803},{"id":"https://openalex.org/C2778780117","wikidata":"https://www.wikidata.org/wiki/Q3269423","display_name":"Reading comprehension","level":3,"score":0.35580000281333923},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.3529999852180481},{"id":"https://openalex.org/C67277372","wikidata":"https://www.wikidata.org/wiki/Q7449085","display_name":"Semantic role labeling","level":3,"score":0.34279999136924744},{"id":"https://openalex.org/C2778828372","wikidata":"https://www.wikidata.org/wiki/Q5283209","display_name":"Distributional semantics","level":3,"score":0.3425999879837036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32519999146461487},{"id":"https://openalex.org/C2777617010","wikidata":"https://www.wikidata.org/wiki/Q18957","display_name":"Mainstream","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.29510000348091125},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C72196577","wikidata":"https://www.wikidata.org/wiki/Q1645946","display_name":"Argument map","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.25780001282691956},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228575","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":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2250999640","https://openalex.org/W2475245295","https://openalex.org/W2891553865","https://openalex.org/W2939100138","https://openalex.org/W2984514053","https://openalex.org/W3024298906","https://openalex.org/W3034999214","https://openalex.org/W3035229828","https://openalex.org/W3092043163","https://openalex.org/W3100192071","https://openalex.org/W3102925419","https://openalex.org/W3103519070","https://openalex.org/W3104597568","https://openalex.org/W3161207330","https://openalex.org/W3170759063","https://openalex.org/W3174870841","https://openalex.org/W3211708814","https://openalex.org/W4225321042","https://openalex.org/W4226263955","https://openalex.org/W4281643888","https://openalex.org/W4287019595","https://openalex.org/W4385567257","https://openalex.org/W4385572346","https://openalex.org/W4388863148","https://openalex.org/W4404239715"],"related_works":[],"abstract_inverted_index":{"Event":[0,66],"argument":[1,35,49,145,156],"extraction":[2,120,146,157],"(EAE),":[3],"a":[4,97],"critical":[5],"task":[6],"in":[7,38,47,111,154,162],"information":[8,15],"extraction,":[9],"aims":[10],"to":[11,25,104],"identify":[12],"structured":[13],"event":[14,32,119],"from":[16,31],"textual":[17],"sentences.":[18],"However,":[19],"existing":[20,160],"Chinese":[21,65,118],"EAE":[22],"methods":[23],"fail":[24],"effectively":[26],"integrate":[27],"prior":[28],"semantic":[29,90],"knowledge":[30],"types":[33],"and":[34,76,123,142],"roles,":[36],"resulting":[37],"suboptimal":[39],"performance.":[40],"Moreover,":[41],"mainstream":[42],"approaches":[43],"exhibit":[44],"limited":[45],"generalization":[46],"few-shot":[48,155],"recognition":[50],"scenarios.":[51],"To":[52],"address":[53],"these":[54],"challenges,":[55],"we":[56,95],"propose":[57],"an":[58],"enhanced":[59],"Machine":[60],"Reading":[61],"Comprehension":[62],"(MRC)-based":[63],"Cross-Domain":[64],"Argument":[67],"Extraction":[68],"framework.":[69],"Our":[70],"approach":[71],"systematically":[72],"incorporates":[73],"event-type":[74],"descriptions":[75],"argument-driven":[77],"question":[78],"formulations":[79],"into":[80],"contextual":[81],"representations":[82],"through":[83],"multi-head":[84],"attention":[85],"mechanisms,":[86],"thereby":[87],"enhancing":[88],"hierarchical":[89],"understanding":[91],"of":[92,109,128],"events.":[93],"Furthermore,":[94],"introduce":[96],"transfer":[98],"learning":[99],"strategy":[100],"with":[101],"boundary-aware":[102],"optimization":[103],"mitigate":[105],"poor":[106],"boundary":[107],"detection":[108],"arguments":[110],"low-resource":[112],"settings.":[113],"Extensive":[114],"experiments":[115],"on":[116,144],"two":[117],"benchmarks,":[121],"ACE2005":[122],"MEC,":[124],"validate":[125],"the":[126],"effectiveness":[127],"our":[129],"method.":[130],"The":[131],"proposed":[132],"model":[133],"achieves":[134],"superior":[135],"performance,":[136],"improving":[137],"F1":[138],"scores":[139],"by":[140],"1.6%":[141],"1.9%":[143],"tasks,":[147],"respectively.":[148],"Notably,":[149],"it":[150],"demonstrates":[151],"significant":[152],"gains":[153],"scenarios,":[158],"outperforming":[159],"baselines":[161],"cross-domain":[163],"generalization.":[164]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}