{"id":"https://openalex.org/W7213465641","doi":"https://doi.org/10.1371/journal.pone.0357708","title":"Construction and optimization of deep learning intent classification model for smart home dialogue","display_name":"Construction and optimization of deep learning intent classification model for smart home dialogue","publication_year":2026,"publication_date":"2026-09-17","ids":{"openalex":"https://openalex.org/W7213465641","doi":"https://doi.org/10.1371/journal.pone.0357708","pmid":"https://pubmed.ncbi.nlm.nih.gov/42752642","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13585276"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pone.0357708","doi":"https://doi.org/10.1371/journal.pone.0357708","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0357708","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357708&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357708&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109774039","display_name":"Qiyuan Lu","orcid":"https://orcid.org/0009-0008-7961-8167","observed_orcids":["https://orcid.org/0009-0008-7961-8167"]},"institutions":[{"id":"https://openalex.org/I4210140336","display_name":"Lanzhou City University","ror":"https://ror.org/03cd4ja39","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210140336"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiyuan Lu","raw_affiliation_strings":["Art and Design School, Lanzhou City University"],"raw_orcid":"https://orcid.org/0009-0008-7961-8167","affiliations":[{"raw_affiliation_string":"Art and Design School, Lanzhou City University","institution_ids":["https://openalex.org/I4210140336"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5109774039"],"corresponding_institution_ids":["https://openalex.org/I4210140336"],"apc_list":{"value":2382,"currency":"USD","value_usd":2382},"apc_paid":{"value":2382,"currency":"USD","value_usd":2382},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6095407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":"9","first_page":"e0357708","last_page":"e0357708"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":0.5835000276565552,"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/T12031","display_name":"Speech and dialogue systems","score":0.5835000276565552,"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/T12222","display_name":"IoT-based Smart Home Systems","score":0.21770000457763672,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.059700001031160355,"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/intent-recognition","display_name":"intent classification","score":0.9020000100135803},{"id":"https://openalex.org/keywords/unsupervised-topic-modeling","display_name":"unsupervised topic modeling","score":0.7620000243186951},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"latent Dirichlet allocation","score":0.9509999752044678},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.9120000004768372},{"id":"https://openalex.org/keywords/bidirectional-transformer","display_name":"bidirectional transformer","score":0.828000009059906},{"id":"https://openalex.org/keywords/user-behavior-analysis","display_name":"user behavior analysis","score":0.5040000081062317},{"id":"https://openalex.org/keywords/device-control","display_name":"device control","score":0.6620000004768372}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7878000140190125},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7821999788284302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7427999973297119},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.7372000217437744},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6887000203132629},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6098999977111816},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.585099995136261},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5016000270843506},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4684000015258789},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4228000044822693},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.38429999351501465},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.34389999508857727},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33550000190734863},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3319000005722046},{"id":"https://openalex.org/C507571656","wikidata":"https://www.wikidata.org/wiki/Q848436","display_name":"Home automation","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3197999894618988},{"id":"https://openalex.org/C2780479914","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Document classification","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.2628999948501587},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C2474386","wikidata":"https://www.wikidata.org/wiki/Q461183","display_name":"Text corpus","level":2,"score":0.25189998745918274}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098430","descriptor_name":"Clustering Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D033182","descriptor_name":"Intention","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.1371/journal.pone.0357708","doi":"https://doi.org/10.1371/journal.pone.0357708","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0357708","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357708&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13585276","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13585276/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13585276/pdf/pone.0357708.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS One","raw_type":"Text"},{"id":"pmid:42752642","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42752642","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PloS one","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:49ae45dc57df43dd9a818055da6913c1","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/49ae45dc57df43dd9a818055da6913c1","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,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS ONE, Vol 21, Iss 9, p e0357708 (2026)","raw_type":"article"},{"id":"pmh:oai:doaj.org/article:70c3cb32d45240e698750f6ab4c36331","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/70c3cb32d45240e698750f6ab4c36331","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,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS ONE, Vol 21, Iss 9 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1371/journal.pone.0357708","doi":"https://doi.org/10.1371/journal.pone.0357708","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0357708","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357708&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7213465641.pdf","grobid_xml":"https://content.openalex.org/works/W7213465641.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W3013860672","https://openalex.org/W3122866338","https://openalex.org/W3162602085","https://openalex.org/W3173488222","https://openalex.org/W3194945445","https://openalex.org/W4206696791","https://openalex.org/W4221094122","https://openalex.org/W4223518348","https://openalex.org/W4229377210","https://openalex.org/W4281613392","https://openalex.org/W4285045211","https://openalex.org/W4285153164","https://openalex.org/W4285985193","https://openalex.org/W4312980540","https://openalex.org/W4315780520","https://openalex.org/W4317936425","https://openalex.org/W4366282389","https://openalex.org/W4380714981","https://openalex.org/W4381855863","https://openalex.org/W4385156317","https://openalex.org/W4385311368","https://openalex.org/W4386700846","https://openalex.org/W4386746169","https://openalex.org/W4392386515","https://openalex.org/W4395689600","https://openalex.org/W4410841389","https://openalex.org/W4411119167"],"related_works":["https://openalex.org/W4410129506","https://openalex.org/W2989982989","https://openalex.org/W2991537901","https://openalex.org/W4412620322","https://openalex.org/W2782096898","https://openalex.org/W4389144618","https://openalex.org/W3212343896","https://openalex.org/W2939782285","https://openalex.org/W2938802551","https://openalex.org/W2990474209"],"abstract_inverted_index":{"In":[0,146],"response":[1],"to":[2,80,108,119],"the":[3,10,16,120,140,143,151,154,164,176,211,217,231,239,243],"lack":[4],"of":[5,21,125,142,148,153,171,246],"high-quality":[6],"annotated":[7],"corpora":[8],"in":[9,26,123,180],"current":[11],"smart":[12,50,247],"home":[13,28,51,248],"field":[14],"and":[15,19,43,64,74,129,135,163,190,206,224,241],"insufficient":[17],"accuracy":[18,152],"interpretability":[20],"existing":[22],"intent":[23,34,111,221],"classification":[24,35,92,222],"models":[25],"complex":[27],"scenarios,":[29],"this":[30],"study":[31],"proposes":[32],"an":[33,65],"model":[36,69,93,101,116,122,160],"system":[37],"that":[38,96,202],"integrates":[39,97],"unsupervised":[40,66,114],"topic":[41,67,115],"discovery":[42],"deep":[44,90],"learning":[45,91],"classification.":[46,112],"Firstly,":[47],"a":[48,58,85,89,98,103],"Chinese":[49],"corpus":[52],"containing":[53],"120,000":[54],"real":[55],"conversations":[56],"with":[57,102],"dual-layer":[59],"annotation":[60],"structure":[61],"is":[62,78,94],"constructed,":[63],"recognition":[68],"combining":[70],"latent":[71],"Dirichlet":[72],"allocation":[73],"K-Means":[75],"clustering":[76],"algorithm":[77],"designed":[79,95],"mine":[81],"semantic":[82,144],"structures":[83],"from":[84],"data-driven":[86],"perspective.":[87],"Finally,":[88],"bidirectional":[99,155],"transformer":[100],"text":[104],"convolutional":[105,157],"neural":[106,158],"network":[107,159],"achieve":[109],"accurate":[110],"The":[113],"was":[117,161,168],"superior":[118],"single":[121],"terms":[124,147],"normalized":[126],"mutual":[127],"information":[128],"adjusted":[130],"Rand":[131],"index":[132],"reaching":[133],"0.752":[134],"0.698":[136],"respectively,":[137],"which":[138,172],"verified":[139],"rationality":[141],"structure.":[145],"supervised":[149],"learning,":[150],"transformer-text":[156],"96.7%,":[162],"macro":[165],"F1":[166],"score":[167,187,194],"96.4%,":[169],"both":[170],"were":[173,210],"better":[174],"than":[175],"baseline":[177],"model,":[178],"especially":[179],"tasks":[181],"such":[182],"as":[183],"\"light":[184],"control\"":[185,204],"(F1":[186,193],"=":[188,195],"98.2%)":[189],"\"temperature":[191],"query\"":[192,208],"97.6%).":[196],"Intent":[197],"distribution":[198],"analysis":[199],"further":[200],"indicated":[201],"\"device":[203],"(31.2%)":[205],"\"status":[207],"(24.2%)":[209],"most":[212],"frequent":[213],"user":[214,227],"needs.":[215],"Overall,":[216],"research":[218],"provides":[219],"high-performance":[220],"solutions,":[223],"also":[225],"reveals":[226],"behavior":[228],"characteristics":[229],"at":[230],"data":[232],"level,":[233],"providing":[234],"important":[235],"basis":[236],"for":[237],"optimizing":[238],"functionality":[240],"enhancing":[242],"interactive":[244],"experience":[245],"systems.":[249]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2026-09-17T00:00:00"}
