{"id":"https://openalex.org/W3191607794","doi":"https://doi.org/10.1109/access.2021.3102685","title":"Nested Named Entity Recognition via an Independent-Layered Pretrained Model","display_name":"Nested Named Entity Recognition via an Independent-Layered Pretrained Model","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3191607794","doi":"https://doi.org/10.1109/access.2021.3102685","mag":"3191607794"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3102685","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3102685","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09507481.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09507481.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072516394","display_name":"Liruizhi Jia","orcid":"https://orcid.org/0000-0002-0178-4375"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liruizhi Jia","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"raw_orcid":"https://orcid.org/0000-0002-0178-4375","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025798843","display_name":"Shengquan Liu","orcid":"https://orcid.org/0000-0001-9623-4714"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengquan Liu","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"raw_orcid":"https://orcid.org/0000-0001-9623-4714","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102929933","display_name":"Fuyuan Wei","orcid":"https://orcid.org/0000-0003-4099-4831"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuyuan Wei","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"raw_orcid":"https://orcid.org/0000-0003-4099-4831","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033203778","display_name":"Bo Kong","orcid":"https://orcid.org/0000-0002-6002-9150"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Kong","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"raw_orcid":"https://orcid.org/0000-0002-6002-9150","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051223879","display_name":"Guangyao Wang","orcid":"https://orcid.org/0000-0003-2212-3100"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyao Wang","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"raw_orcid":"https://orcid.org/0000-0003-2212-3100","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6995,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76352248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"9","issue":null,"first_page":"109693","last_page":"109703"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9993000030517578,"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/T11719","display_name":"Data Quality and Management","score":0.991599977016449,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8263429999351501},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.7768845558166504},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.7545726895332336},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.6403461694717407},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5325151085853577},{"id":"https://openalex.org/keywords/nested-set-model","display_name":"Nested set model","score":0.5289259552955627},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5202354192733765},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.507239043712616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47774213552474976},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4625180959701538},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4409652650356293},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4176284074783325},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3316260278224945},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2689729332923889},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09744387865066528}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8263429999351501},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.7768845558166504},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.7545726895332336},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.6403461694717407},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5325151085853577},{"id":"https://openalex.org/C103000020","wikidata":"https://www.wikidata.org/wiki/Q1978426","display_name":"Nested set model","level":3,"score":0.5289259552955627},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5202354192733765},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.507239043712616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47774213552474976},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4625180959701538},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4409652650356293},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4176284074783325},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3316260278224945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2689729332923889},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09744387865066528},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","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},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.0},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3102685","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3102685","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09507481.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1b7ec686789649289296ad83694bc3c6","is_oa":true,"landing_page_url":"https://doaj.org/article/1b7ec686789649289296ad83694bc3c6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 109693-109703 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3102685","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3102685","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09507481.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6967985870","display_name":"\u8206\u60c5\u672c\u4f53\u6982\u5ff5\u95f4\u975e\u5206\u7c7b\u5173\u7cfb\u62bd\u53d6\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61966034","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3191607794.pdf","grobid_xml":"https://content.openalex.org/works/W3191607794.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W2047477415","https://openalex.org/W2108218871","https://openalex.org/W2134036914","https://openalex.org/W2147880316","https://openalex.org/W2163107094","https://openalex.org/W2250710764","https://openalex.org/W2407338347","https://openalex.org/W2760146825","https://openalex.org/W2803609931","https://openalex.org/W2804221886","https://openalex.org/W2864258299","https://openalex.org/W2892252202","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2938830017","https://openalex.org/W2949257977","https://openalex.org/W2949952998","https://openalex.org/W2950175955","https://openalex.org/W2950281195","https://openalex.org/W2952594430","https://openalex.org/W2962902328","https://openalex.org/W2963186636","https://openalex.org/W2963299810","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963563735","https://openalex.org/W2963568202","https://openalex.org/W2963571583","https://openalex.org/W2964015378","https://openalex.org/W2971019153","https://openalex.org/W2996762458","https://openalex.org/W3011574394","https://openalex.org/W3034744126","https://openalex.org/W3035543689","https://openalex.org/W3088409176","https://openalex.org/W3090015871","https://openalex.org/W3102603416","https://openalex.org/W3106312823","https://openalex.org/W4385245566","https://openalex.org/W6682082992","https://openalex.org/W6714112401","https://openalex.org/W6726873649","https://openalex.org/W6739901393","https://openalex.org/W6753428393","https://openalex.org/W6755207826","https://openalex.org/W6761910064","https://openalex.org/W6767061844"],"related_works":["https://openalex.org/W2035329725","https://openalex.org/W4376641153","https://openalex.org/W2070875936","https://openalex.org/W4250391473","https://openalex.org/W3045075405","https://openalex.org/W4302292679","https://openalex.org/W2956222435","https://openalex.org/W4241625287","https://openalex.org/W2050788868","https://openalex.org/W4285357211"],"abstract_inverted_index":{"When":[0],"an":[1],"entity":[2,107],"contains":[3],"one":[4],"or":[5],"more":[6,133],"entities,":[7,54],"these":[8,118],"particular":[9],"entities":[10,84],"are":[11,149],"referred":[12],"to":[13,26,59,78,116,196],"as":[14,31],"nested":[15,28,105],"entities.":[16,29],"The":[17,82,138,152,172],"Layered":[18],"BiLSTM-CRF":[19],"model":[20,43,58,64,131,139,192,204],"can":[21,44],"use":[22],"multiple":[23],"BiLSTM":[24],"layers":[25,35,115,180,190],"identify":[27],"However,":[30],"the":[32,37,42,57,63,69,75,79,87,92,95,160,165,169,214],"number":[33,38],"of":[34,39,72,94,141,154,159,208],"increases,":[36],"labels":[40],"that":[41,110,176,202],"learn":[45],"decreases,":[46],"and":[47,146,194,211,217],"it":[48],"may":[49],"not":[50,182],"even":[51],"predict":[52],"any":[53],"thereby":[55],"causing":[56],"stop":[60],"stacking.":[61],"Furthermore,":[62],"will":[65,90,181],"be":[66,183],"constrained":[67],"by":[68,86,164,186],"one-way":[70],"propagation":[71],"information":[73,126],"from":[74],"lower":[76],"layer":[77,89,123,145,156],"higher":[80],"layer.":[81,97,171],"incorrect":[83],"extracted":[85],"outer":[88],"affect":[91],"performance":[93],"inner":[96],"We":[98],"propose":[99],"a":[100,129,142],"novel":[101],"neural":[102],"network":[103],"for":[104],"named":[106],"recognition":[108],"(NER)":[109],"dynamically":[111],"stacks":[112],"flat":[113,121,143,178,188],"NER":[114,122,144,179,189],"address":[117],"issues.":[119],"Each":[120],"captures":[124],"contextual":[125],"based":[127],"on":[128,213],"pretrained":[130],"with":[132,185],"robust":[134],"feature":[135],"extraction":[136],"capabilities.":[137],"parameters":[140],"its":[147],"input":[148,153,166,174],"entirely":[150],"independent.":[151],"each":[155],"is":[157],"all":[158],"word":[161],"representations":[162],"generated":[163],"sequence":[167],"through":[168],"embedding":[170],"independent":[173],"ensures":[175],"different":[177],"interfered":[184],"other":[187],"during":[191],"training":[193],"testing":[195],"reduce":[197],"error":[198],"propagation.":[199],"Experiments":[200],"show":[201],"our":[203],"obtains":[205],"F1":[206],"scores":[207],"76.9%,":[209],"78.1%,":[210],"78.0%":[212],"ACE2004,":[215],"ACE2005,":[216],"GENIA":[218],"datasets,":[219],"respectively.":[220]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
