{"id":"https://openalex.org/W4392255678","doi":"https://doi.org/10.1145/3633637.3633646","title":"Chinese Event Extraction Algorithm of Multi-Information Semantic Enhancements","display_name":"Chinese Event Extraction Algorithm of Multi-Information Semantic Enhancements","publication_year":2023,"publication_date":"2023-10-27","ids":{"openalex":"https://openalex.org/W4392255678","doi":"https://doi.org/10.1145/3633637.3633646"},"language":"en","primary_location":{"id":"doi:10.1145/3633637.3633646","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3633637.3633646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 12th International Conference on Computing and Pattern Recognition","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/A5008213911","display_name":"Baohua Qiang","orcid":"https://orcid.org/0000-0002-3469-6590"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baohua Qiang","raw_affiliation_strings":["Guilin University of Electronic Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-3469-6590","affiliations":[{"raw_affiliation_string":"Guilin University of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101404870","display_name":"Feng Tang","orcid":"https://orcid.org/0009-0005-2343-7437"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Tang","raw_affiliation_strings":["Guilin University of Electronic Technology, China"],"raw_orcid":"https://orcid.org/0009-0005-2343-7437","affiliations":[{"raw_affiliation_string":"Guilin University of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072946228","display_name":"Xiangyu Zhou","orcid":"https://orcid.org/0000-0003-0702-8447"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyu Zhou","raw_affiliation_strings":["Guilin University of Electronic Technology, China"],"raw_orcid":"https://orcid.org/0000-0003-0702-8447","affiliations":[{"raw_affiliation_string":"Guilin University of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086132916","display_name":"Yuanchun Wang","orcid":"https://orcid.org/0009-0002-3250-5954"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuanchun Wang","raw_affiliation_strings":["The 54th Research Institute of CETC, China"],"raw_orcid":"https://orcid.org/0009-0002-3250-5954","affiliations":[{"raw_affiliation_string":"The 54th Research Institute of CETC, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067186052","display_name":"Xianyi Yang","orcid":"https://orcid.org/0000-0002-9026-7934"},"institutions":[{"id":"https://openalex.org/I5343935","display_name":"Guilin University of Electronic Technology","ror":"https://ror.org/05arjae42","country_code":"CN","type":"education","lineage":["https://openalex.org/I5343935"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyi Yang","raw_affiliation_strings":["Guilin University of Electronic Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-9026-7934","affiliations":[{"raw_affiliation_string":"Guilin University of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103264467","display_name":"Kai Li","orcid":"https://orcid.org/0009-0003-6831-4292"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Li","raw_affiliation_strings":["The 54th Research Institute of CETC, China"],"raw_orcid":"https://orcid.org/0009-0003-6831-4292","affiliations":[{"raw_affiliation_string":"The 54th Research Institute of CETC, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"58","last_page":"64"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9973999857902527,"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":0.9973999857902527,"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":0.9961000084877014,"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/T10679","display_name":"Service-Oriented Architecture and Web Services","score":0.9850999712944031,"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/computer-science","display_name":"Computer science","score":0.7810229659080505},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.6338822245597839},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6048480272293091},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5762222409248352},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5634903311729431},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5315097570419312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5124054551124573},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4837093949317932},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4775698781013489},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45963841676712036},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41765230894088745},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3638949692249298},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3445506989955902},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12285304069519043}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7810229659080505},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.6338822245597839},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6048480272293091},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5762222409248352},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5634903311729431},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5315097570419312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5124054551124573},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4837093949317932},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4775698781013489},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45963841676712036},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41765230894088745},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3638949692249298},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3445506989955902},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12285304069519043},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3633637.3633646","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3633637.3633646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 12th International Conference on Computing and Pattern Recognition","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":13,"referenced_works":["https://openalex.org/W2079725295","https://openalex.org/W3011574394","https://openalex.org/W3021636956","https://openalex.org/W3088409176","https://openalex.org/W3092043163","https://openalex.org/W3102725307","https://openalex.org/W3138131302","https://openalex.org/W3159043356","https://openalex.org/W3168478035","https://openalex.org/W3189812067","https://openalex.org/W3216961012","https://openalex.org/W4389790865","https://openalex.org/W6602430550"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2161474341","https://openalex.org/W2037549926","https://openalex.org/W4302615923","https://openalex.org/W3203142394","https://openalex.org/W2351061015","https://openalex.org/W4220731478","https://openalex.org/W1974101135","https://openalex.org/W2017509870","https://openalex.org/W4286432911"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,18,22,35,45,56,59,71,86,93,117,123,133,144,148,153,158,171],"problems":[3],"of":[4,21,50,97,155],"inaccurate":[5],"event":[6,39,98,167,181],"element":[7],"extraction,":[8],"inability":[9],"to":[10,25,106,131,136,151],"directly":[11],"handle":[12],"long":[13],"texts":[14],"and":[15,29,81,84,102,127,175],"errors":[16],"in":[17,34,184],"fine-tuning":[19],"stage":[20],"task":[23],"due":[24],"insufficient":[26],"semantic":[27,31,138],"information":[28,101,105,139],"incomplete":[30],"feature":[32],"samples":[33],"current":[36],"Chinese":[37,46,141,166],"domain":[38],"extraction":[40,168,177,182],"model,":[41],"this":[42,162],"paper":[43],"proposes":[44],"Event":[47],"Extraction":[48],"Algorithm":[49],"Multi-Information":[51],"Semantic":[52],"Enhancements":[53],"(MiSE).":[54],"In":[55,143],"embedding":[57,80,89,95],"stage,":[58,146],"algorithm":[60,163],"is":[61,119],"designed":[62],"a":[63],"coding":[64],"structure":[65,150],"that":[66,113],"fusing":[67],"multiple":[68,115],"information:":[69],"Using":[70],"Roformer":[72,87],"model":[73],"based":[74],"on":[75,170],"rotational":[76],"encoding":[77],"for":[78,125],"word":[79,88,94],"text":[82],"embedding,":[83],"augment":[85],"matrix":[90,112,118],"by":[91],"mapping":[92],"matrixs":[96],"trigger,":[99],"argument":[100],"their":[103],"location":[104],"it":[107],",":[108],"forming":[109],"an":[110],"vector":[111],"incorporates":[114],"information;":[116],"then":[120],"placed":[121],"into":[122],"BiLSTM":[124],"forward":[126],"backward":[128],"chain":[129],"calculation":[130],"enhance":[132],"model's":[134],"ability":[135],"extract":[137],"from":[140],"text.":[142],"decoding":[145,149],"using":[147],"obtain":[152],"set":[154],"tags":[156],"with":[157,164],"highest":[159],"probability.":[160],"Comparing":[161],"several":[165],"algorithms":[169,183],"Baidu":[172],"DuEE":[173],"dataset":[174],"its":[176],"results":[178],"outperformed":[179],"other":[180],"all":[185],"evaluation":[186],"metrics.":[187]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
