{"id":"https://openalex.org/W4226069610","doi":"https://doi.org/10.1109/ccis53392.2021.9754636","title":"An Extraction Model Based on RoBERTa-BiLSTM-CRF for Chinese Financial Event","display_name":"An Extraction Model Based on RoBERTa-BiLSTM-CRF for Chinese Financial Event","publication_year":2021,"publication_date":"2021-11-07","ids":{"openalex":"https://openalex.org/W4226069610","doi":"https://doi.org/10.1109/ccis53392.2021.9754636"},"language":"en","primary_location":{"id":"doi:10.1109/ccis53392.2021.9754636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis53392.2021.9754636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","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/A5081637055","display_name":"Dagao Duan","orcid":"https://orcid.org/0000-0003-3475-2000"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dagao Duan","raw_affiliation_strings":["Beijing Technology and Business University,School of International Economics and Management,Beijing,China,100048"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University,School of International Economics and Management,Beijing,China,100048","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343176","display_name":"Wenwen Liu","orcid":"https://orcid.org/0000-0003-1082-4401"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwen Liu","raw_affiliation_strings":["Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,100048"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,100048","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047135195","display_name":"Zhongming Han","orcid":"https://orcid.org/0000-0003-0119-2828"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongming Han","raw_affiliation_strings":["Beijing Technology and Business University,School of International Economics and Management,Beijing,China,100048"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University,School of International Economics and Management,Beijing,China,100048","institution_ids":["https://openalex.org/I179026463"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I179026463"],"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":"480","last_page":"486"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9726999998092651,"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/T10028","display_name":"Topic Modeling","score":0.9261000156402588,"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/conditional-random-field","display_name":"Conditional random field","score":0.8576163053512573},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6987329125404358},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6334102153778076},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5989134311676025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5965867042541504},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5515501499176025},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4760127663612366},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.46452105045318604},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.453203946352005},{"id":"https://openalex.org/keywords/recall-rate","display_name":"Recall rate","score":0.4467089772224426},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.44321349263191223},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.4360455274581909},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.428352028131485},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4086127281188965},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37816908955574036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3534911870956421},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12134599685668945}],"concepts":[{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.8576163053512573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6987329125404358},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6334102153778076},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5989134311676025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5965867042541504},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5515501499176025},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4760127663612366},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.46452105045318604},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.453203946352005},{"id":"https://openalex.org/C2987098735","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"Recall rate","level":2,"score":0.4467089772224426},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.44321349263191223},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.4360455274581909},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.428352028131485},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4086127281188965},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37816908955574036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3534911870956421},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12134599685668945},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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:10.1109/ccis53392.2021.9754636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis53392.2021.9754636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1520232900","https://openalex.org/W2046491822","https://openalex.org/W2250575108","https://openalex.org/W2250999640","https://openalex.org/W2475245295","https://openalex.org/W2539274330","https://openalex.org/W2803884531","https://openalex.org/W2952437275","https://openalex.org/W2967414584","https://openalex.org/W2970684294","https://openalex.org/W3024298906","https://openalex.org/W3085416163","https://openalex.org/W3092043163","https://openalex.org/W6761205572"],"related_works":["https://openalex.org/W4250494529","https://openalex.org/W1964783010","https://openalex.org/W2399696375","https://openalex.org/W45206245","https://openalex.org/W2211396092","https://openalex.org/W2078793151","https://openalex.org/W2061834489","https://openalex.org/W2751906762","https://openalex.org/W3217585508","https://openalex.org/W3201257042"],"abstract_inverted_index":{"Event":[0],"Extraction":[1,11],"is":[2],"one":[3],"of":[4,9,25,44,55,62],"the":[5,42,60,101,108,124,129],"important":[6],"research":[7],"tasks":[8],"Information":[10],"in":[12,31],"Natural":[13],"Language":[14],"Processing.":[15],"It":[16,78],"tries":[17],"to":[18,52,119],"extract":[19],"information":[20,30],"from":[21,115],"a":[22,32,74],"large":[23],"amount":[24],"chaotic":[26],"data":[27],"and":[28,93,142,151],"presents":[29],"structural":[33],"form.":[34],"The":[35],"existing":[36,147],"Chinese":[37,45,56,69,102,131],"event":[38,63,71,81,104,133],"extraction":[39,72,82,134],"methods":[40],"have":[41],"inaccuracies":[43],"word":[46],"segmentation,":[47],"which":[48],"will":[49],"directly":[50],"lead":[51],"incorrect":[53],"identification":[54],"financial":[57,70,103,111,132],"entities,":[58],"affecting":[59],"accuracy":[61],"element":[64],"extraction.":[65],"This":[66],"paper":[67,99],"takes":[68],"as":[73],"sequence":[75],"labeling":[76],"task.":[77],"proposes":[79],"an":[80],"model":[83,135],"based":[84],"on":[85,149],"PreTraining":[86],"Model,":[87],"Bidirectional":[88],"Long-Short":[89],"Term":[90],"Memory":[91],"Network,":[92],"Conditional":[94],"Random":[95],"Field.":[96],"Additionally,":[97],"this":[98],"constructs":[100],"dataset":[105,117,121],"FinEE.":[106],"At":[107],"same":[109],"time,":[110],"events":[112],"are":[113],"filtered":[114],"public":[116],"DuEE":[118],"construct":[120],"DuEE_Fin.":[122],"As":[123],"experimental":[125],"results":[126],"show":[127],"that":[128],"proposed":[130],"Roberta-BilSTM-CRF":[136],"has":[137],"improved":[138],"accuracy,":[139],"recall":[140],"rate,":[141],"F1":[143],"score":[144],"compared":[145],"with":[146],"models":[148],"FinEE":[150],"DuEE_Fin":[152],"datasets.":[153]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}