{"id":"https://openalex.org/W3009129493","doi":"https://doi.org/10.1145/3374587.3374611","title":"An Ensemble Deep Active Learning Method for Intent Classification","display_name":"An Ensemble Deep Active Learning Method for Intent Classification","publication_year":2019,"publication_date":"2019-12-06","ids":{"openalex":"https://openalex.org/W3009129493","doi":"https://doi.org/10.1145/3374587.3374611","mag":"3009129493"},"language":"en","primary_location":{"id":"doi:10.1145/3374587.3374611","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3374587.3374611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence","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/A5047710707","display_name":"Leihan Zhang","orcid":"https://orcid.org/0000-0003-0978-6698"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]},{"id":"https://openalex.org/I4210099741","display_name":"Yonyou (China)","ror":"https://ror.org/016dc7q50","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210099741"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Leihan Zhang","raw_affiliation_strings":["Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, P. R. China","Wangxuan Institute of Computer Technology, Peking University, Beijing, P. R. China, Yonyou Network Co Ltd, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, P. R. China","institution_ids":["https://openalex.org/I78675632"]},{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, P. R. China, Yonyou Network Co Ltd, Beijing, P. R. China","institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210099741"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100350631","display_name":"Le Zhang","orcid":"https://orcid.org/0000-0002-6930-8674"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210099741","display_name":"Yonyou (China)","ror":"https://ror.org/016dc7q50","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210099741"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Zhang","raw_affiliation_strings":["Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, P. R. China","Wangxuan Institute of Computer Technology, Peking University, Beijing, P. R. China, Yonyou Network Co Ltd, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, P. R. China","institution_ids":["https://openalex.org/I78675632"]},{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, P. R. China, Yonyou Network Co Ltd, Beijing, P. R. China","institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210099741"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5047710707"],"corresponding_institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210099741","https://openalex.org/I78675632"],"apc_list":null,"apc_paid":null,"fwci":0.9801,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.82920895,"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":"107","last_page":"111"},"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/T12031","display_name":"Speech and dialogue systems","score":0.996999979019165,"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":0.9961000084877014,"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":0.8156105279922485},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.756385326385498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7532377243041992},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6996533870697021},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6426193118095398},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6174085140228271},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5594475269317627},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.48739415407180786},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.42621827125549316},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4186038374900818},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09628614783287048}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8156105279922485},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.756385326385498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7532377243041992},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6996533870697021},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6426193118095398},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6174085140228271},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5594475269317627},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.48739415407180786},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42621827125549316},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4186038374900818},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09628614783287048},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3374587.3374611","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3374587.3374611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W2597787948","https://openalex.org/W2602856279","https://openalex.org/W2626778328","https://openalex.org/W2788686132","https://openalex.org/W2895750630","https://openalex.org/W2963902936","https://openalex.org/W2963974889","https://openalex.org/W2964282813","https://openalex.org/W2979627741","https://openalex.org/W3145013517","https://openalex.org/W3210120707","https://openalex.org/W6600558321"],"related_works":["https://openalex.org/W2981877337","https://openalex.org/W4292388283","https://openalex.org/W3204418343","https://openalex.org/W3203938600","https://openalex.org/W4286910063","https://openalex.org/W2163707935","https://openalex.org/W3132602785","https://openalex.org/W83146503","https://openalex.org/W2169074127","https://openalex.org/W1560624709"],"abstract_inverted_index":{"Intent":[0],"classification":[1,25,89],"plays":[2],"a":[3],"primary":[4],"and":[5,29,37,68,86,122],"critical":[6],"role":[7],"in":[8,135],"intelligent":[9],"dialogue":[10],"systems.":[11],"However,":[12],"faced":[13],"with":[14,104],"the":[15,20,33,93,109,114,117,129],"lack":[16],"of":[17,22,108,116],"labeled":[18],"data,":[19],"training":[21,110],"robust":[23],"intent":[24,63,88,137],"model":[26,36],"is":[27,120],"time-consuming":[28],"costly.":[30],"Thanks":[31],"to":[32,42,47,74],"powerful":[34],"pre-trained":[35],"active":[38,58,97],"learning,":[39],"it's":[40],"possible":[41],"construct":[43],"an":[44,55,70],"integrated":[45],"method":[46,73,99,119,131],"fulfill":[48],"this":[49],"task":[50],"efficiently.":[51],"Therefore,":[52],"we":[53],"propose":[54],"ensemble":[56,71,95],"deep":[57,96],"learning":[59,98],"method,":[60],"which":[61],"constructs":[62],"classifier":[64,138],"based":[65],"on":[66,83],"BERT":[67],"uses":[69],"sampling":[72],"choose":[75],"informative":[76],"data":[77],"for":[78,124],"efficient":[79],"training.":[80],"Experimental":[81],"results":[82],"both":[84,125],"Chinese":[85],"English":[87],"datasets":[90],"suggest":[91],"that":[92],"proposed":[94,118,130],"can":[100],"achieve":[101],"state-of-the-art":[102],"performance":[103,115],"less":[105],"than":[106],"half":[107],"data.":[111],"In":[112,127],"addition,":[113],"stable":[121],"scalable":[123],"datasets.":[126,141],"general,":[128],"shows":[132],"substantial":[133],"advantages":[134],"building":[136],"across":[139],"different":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
