{"id":"https://openalex.org/W3094167405","doi":"https://doi.org/10.1145/3422713.3422742","title":"Chinese Conference Event Named Entity Recognition Based on BERT-BiLSTM-CRF","display_name":"Chinese Conference Event Named Entity Recognition Based on BERT-BiLSTM-CRF","publication_year":2020,"publication_date":"2020-09-18","ids":{"openalex":"https://openalex.org/W3094167405","doi":"https://doi.org/10.1145/3422713.3422742","mag":"3094167405"},"language":"en","primary_location":{"id":"doi:10.1145/3422713.3422742","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3422713.3422742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 3rd International Conference on Big Data Technologies","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/A5101786339","display_name":"Rui Xiong","orcid":"https://orcid.org/0009-0003-8402-7920"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rui Xiong","raw_affiliation_strings":["Beijing Institute of Science and Technology, Information Shoujian Financial Center, Beijing"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Science and Technology, Information Shoujian Financial Center, Beijing","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101786339"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.55474646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"188","last_page":"191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9983000159263611,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.991100013256073,"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":0.7385764122009277},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.716302752494812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6883939504623413},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.6089104413986206},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5538873076438904},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.5367274284362793},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.535089910030365},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.5198690891265869},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4649677574634552},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.1498153805732727},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08329620957374573},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06464812159538269}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7385764122009277},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.716302752494812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6883939504623413},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.6089104413986206},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5538873076438904},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.5367274284362793},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.535089910030365},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5198690891265869},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4649677574634552},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.1498153805732727},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08329620957374573},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06464812159538269},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3422713.3422742","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3422713.3422742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 3rd International Conference on Big Data Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2626680156","https://openalex.org/W2806114307","https://openalex.org/W2905273891","https://openalex.org/W2952594430","https://openalex.org/W3011177673"],"related_works":["https://openalex.org/W4205454697","https://openalex.org/W2621151902","https://openalex.org/W3094868181","https://openalex.org/W3116252395","https://openalex.org/W4281560212","https://openalex.org/W3180764077","https://openalex.org/W2769084352","https://openalex.org/W4312858192","https://openalex.org/W4386322467","https://openalex.org/W3107535086"],"abstract_inverted_index":{"Conference":[0],"events":[1,108],"are":[2],"an":[3],"important":[4],"place":[5],"for":[6,22,71],"people":[7],"to":[8,38],"express":[9],"their":[10],"views.":[11],"Chinese":[12,40,106],"conference":[13,41,107],"event":[14,42],"named":[15,43,109],"entity":[16,110],"recognition":[17],"is":[18,36,49,65],"a":[19,31,46],"key":[20],"technology":[21],"public":[23],"opinion":[24],"tracking":[25],"of":[26,96,99,103],"people.":[27],"In":[28],"this":[29],"paper,":[30],"method":[32],"based":[33,55],"on":[34,56],"BERT-BiLSTM-CRF":[35,80,91],"proposed":[37],"recognize":[39],"entities.":[44],"First,":[45],"character":[47,63],"vector":[48,64],"generated":[50],"by":[51],"the":[52,61,68],"BERT":[53],"model":[54,70,81,87,92],"context":[57],"information.":[58],"And":[59],"then":[60],"trained":[62],"input":[66],"into":[67],"BiLSTM-CRF":[69,86],"further":[72],"training":[73],"processing.":[74],"The":[75],"experimental":[76],"results":[77],"show":[78],"that":[79],"performs":[82],"better":[83],"than":[84],"classical":[85],"and":[88,101],"word2vec-BiLSTM-CRF":[89],"model.":[90],"can":[93],"achieve":[94],"precision":[95],"91.39%,":[97],"recall":[98],"92.31%":[100],"F1-score":[102],"91.85%":[104],"in":[105],"recognition.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}