{"id":"https://openalex.org/W4214596881","doi":"https://doi.org/10.1145/3488933.3488970","title":"Text Classification Method Based on BiGRU-Attention and CNN Hybrid Model","display_name":"Text Classification Method Based on BiGRU-Attention and CNN Hybrid Model","publication_year":2021,"publication_date":"2021-09-24","ids":{"openalex":"https://openalex.org/W4214596881","doi":"https://doi.org/10.1145/3488933.3488970"},"language":"en","primary_location":{"id":"doi:10.1145/3488933.3488970","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488933.3488970","pdf_url":null,"source":{"id":"https://openalex.org/S4363608564","display_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","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 4th International Conference on Artificial Intelligence and Pattern Recognition","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/A5063032857","display_name":"Teng Jinbao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Teng Jinbao","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008233279","display_name":"Weiwei Kong","orcid":"https://orcid.org/0000-0003-2144-4236"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kong Weiwei","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102444688","display_name":"Yidan Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I91656880","display_name":"China Medical University","ror":"https://ror.org/032d4f246","country_code":"CN","type":"education","lineage":["https://openalex.org/I91656880"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Yidan","raw_affiliation_strings":["China Medical University, China"],"affiliations":[{"raw_affiliation_string":"China Medical University, China","institution_ids":["https://openalex.org/I91656880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101151858","display_name":"Tian Qiaoxin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Qiaoxin","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035711293","display_name":"Shi Chenyuan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shi Chenyuan","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100408780","display_name":"Long Li","orcid":"https://orcid.org/0000-0002-7693-9722"},"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":"Li Long","raw_affiliation_strings":["Guilin University Of Electronic Technology, China"],"affiliations":[{"raw_affiliation_string":"Guilin University Of Electronic Technology, China","institution_ids":["https://openalex.org/I5343935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5063032857"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.8883,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.78661861,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"614","last_page":"622"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9804999828338623,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9804999828338623,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9284999966621399,"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":0.9248999953269958,"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/computer-science","display_name":"Computer science","score":0.7489681839942932},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7082369923591614},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6720292568206787},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6039370894432068},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5949310064315796},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5489620566368103},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.531780481338501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5270410180091858},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4420505166053772},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33570805191993713},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30390307307243347},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16127970814704895}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7489681839942932},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7082369923591614},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6720292568206787},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6039370894432068},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5949310064315796},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5489620566368103},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.531780481338501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5270410180091858},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4420505166053772},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33570805191993713},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30390307307243347},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16127970814704895},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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:10.1145/3488933.3488970","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488933.3488970","pdf_url":null,"source":{"id":"https://openalex.org/S4363608564","display_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","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 4th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W386946596","https://openalex.org/W1532008752","https://openalex.org/W1965398296","https://openalex.org/W2058200059","https://openalex.org/W2084766969","https://openalex.org/W2898235869","https://openalex.org/W2912278761","https://openalex.org/W2963477629","https://openalex.org/W2969545244","https://openalex.org/W2970522457","https://openalex.org/W2991252721","https://openalex.org/W3028860567","https://openalex.org/W3087758240","https://openalex.org/W3128590715","https://openalex.org/W3130997606","https://openalex.org/W4255328724","https://openalex.org/W4285533978","https://openalex.org/W4301856958"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W2964954556"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,18,24,47,51,55,66,70,81,85,89,97,103,112,124,139],"problem":[3],"that":[4,123],"traditional":[5],"Gated":[6],"Recurrent":[7],"Unit":[8],"(GRU)":[9],"and":[10,37,53,72,109,132],"Convolution":[11],"Neural":[12],"Network":[13],"(CNN)":[14],"can":[15,136],"not":[16],"reflect":[17],"importance":[19],"of":[20,50,69,84,91,99,106,141],"each":[21],"word":[22],"in":[23],"text":[25,30,107,142],"when":[26],"extracting":[27],"features,":[28],"a":[29],"classification":[31],"method":[32],"based":[33],"on":[34,111,118],"BiGRU":[35,61,78,92],"Attention":[36],"CNN":[38,42,100,131],"is":[39],"proposed.":[40],"Firstly,":[41],"was":[43,58,62,75,94,127],"used":[44,63,76],"to":[45,64,79,101],"extract":[46,65,80],"local":[48],"information":[49],"text,":[52,71],"then":[54],"full-text":[56],"semantics":[57],"integrated.":[59],"Secondly,":[60],"context":[67],"features":[68,108],"attention":[73,82,93],"mechanism":[74],"after":[77],"score":[83],"output":[86,90,98],"information.":[87],"Finally,":[88],"fused":[95],"with":[96],"realize":[102],"effective":[104],"extraction":[105],"focused":[110],"important":[113],"content":[114],"words.":[115],"Experimental":[116],"results":[117],"three":[119],"public":[120],"datasets":[121],"showed":[122],"proposed":[125],"model":[126],"better":[128],"than":[129],"GRU,":[130],"other":[133],"models,":[134],"which":[135],"effectively":[137],"improve":[138],"effect":[140],"classification.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
