{"id":"https://openalex.org/W4206250065","doi":"https://doi.org/10.1109/icct52962.2021.9657937","title":"A Method for Resume Information Extraction Using BERT-BiLSTM-CRF","display_name":"A Method for Resume Information Extraction Using BERT-BiLSTM-CRF","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W4206250065","doi":"https://doi.org/10.1109/icct52962.2021.9657937"},"language":"en","primary_location":{"id":"doi:10.1109/icct52962.2021.9657937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9657937","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","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/A5023380073","display_name":"Xiaowei Li","orcid":"https://orcid.org/0000-0002-0874-814X"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"XiaoWei Li","raw_affiliation_strings":["State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103078504","display_name":"Hui Shu","orcid":"https://orcid.org/0000-0002-2797-1355"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Shu","raw_affiliation_strings":["State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101242927","display_name":"Yi Zhai","orcid":"https://orcid.org/0000-0001-8461-9705"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Zhai","raw_affiliation_strings":["State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China","institution_ids":["https://openalex.org/I169689159"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079430794","display_name":"Zhiqiang Lin","orcid":"https://orcid.org/0000-0001-8486-564X"},"institutions":[{"id":"https://openalex.org/I169689159","display_name":"PLA Information Engineering University","ror":"https://ror.org/00mm1qk40","country_code":"CN","type":"education","lineage":["https://openalex.org/I169689159"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ZhiQiang Lin","raw_affiliation_strings":["State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of MEAC, Information Engineering University, Zhengzhou, China","institution_ids":["https://openalex.org/I169689159"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I169689159"],"apc_list":null,"apc_paid":null,"fwci":2.491,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.91741085,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1437","last_page":"1442"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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":0.9994000196456909,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.998199999332428,"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":0.9879000186920166,"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":0.7670637369155884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5884526968002319},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5348035097122192},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5206657648086548},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5145348906517029},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44151780009269714},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.35501372814178467},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3504561185836792},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34780144691467285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7670637369155884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5884526968002319},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5348035097122192},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5206657648086548},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5145348906517029},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44151780009269714},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35501372814178467},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3504561185836792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34780144691467285},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icct52962.2021.9657937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9657937","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5099999904632568,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W295894637","https://openalex.org/W1975568626","https://openalex.org/W2099196804","https://openalex.org/W2147880316","https://openalex.org/W2402268235","https://openalex.org/W2558827432","https://openalex.org/W2572428969","https://openalex.org/W2755350660","https://openalex.org/W2857028992","https://openalex.org/W2896457183","https://openalex.org/W2899849645","https://openalex.org/W2900868419","https://openalex.org/W2963738950","https://openalex.org/W2987988965","https://openalex.org/W3002226419","https://openalex.org/W3010362629","https://openalex.org/W3018648280","https://openalex.org/W3093673666"],"related_works":["https://openalex.org/W2397288865","https://openalex.org/W2368524271","https://openalex.org/W2576709312","https://openalex.org/W2079402751","https://openalex.org/W2392797073","https://openalex.org/W2989490741","https://openalex.org/W2023657818","https://openalex.org/W2384907669","https://openalex.org/W2373120800","https://openalex.org/W2368651715"],"abstract_inverted_index":{"To":[0],"solve":[1],"the":[2,42,49,95,109,113,124,134,137],"problem":[3],"of":[4,7,55,99,136],"low":[5],"efficiency":[6],"electronic":[8,130],"resume":[9,17,43,56,115,131,138],"information":[10,18,117,139],"extraction":[11,19,140],"by":[12],"artificial":[13],"construction":[14],"rules,":[15],"a":[16],"method":[20],"based":[21,70,142],"on":[22,71,143],"named":[23,45],"entity":[24,46,116],"recognition":[25],"is":[26,58,75,91,118,145],"proposed,":[27],"which":[28],"extracted":[29,59],"personal":[30],"details":[31],"such":[32],"as":[33],"graduation":[34],"college,":[35],"job":[36,39],"intention":[37],"and":[38,63,81,107,133],"skills":[40],"from":[41],"into":[44],"recognition.":[47],"Firstly,":[48],"TXT":[50],"text":[51,79],"in":[52],"different":[53],"formats":[54],"file":[57],"for":[60],"data":[61],"cleaning":[62],"other":[64,148],"preprocessing.":[65],"The":[66,87],"BERT":[67],"language":[68],"model":[69,141],"multi-head":[72],"self-attention":[73],"mechanism":[74],"used":[76,92],"to":[77,93,105],"extract":[78,129],"features":[80,98],"obtain":[82,94],"word":[83],"granularity":[84],"vector":[85],"matrix.":[86],"BiLSTM":[88],"neural":[89],"network":[90],"context":[96],"abstraction":[97],"serialized":[100],"text.":[101],"Finally,":[102],"using":[103],"CRF":[104],"decode":[106],"annotate":[108],"global":[110],"optimal":[111],"sequence,":[112],"corresponding":[114],"extracted.":[119],"Experimental":[120],"results":[121],"show":[122],"that":[123],"whole":[125],"scheme":[126],"can":[127],"effectively":[128],"information,":[132],"performance":[135],"BERT-BiLSTM-CRF":[144],"better":[146],"than":[147],"models.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
