{"id":"https://openalex.org/W2399691617","doi":"https://doi.org/10.1109/icassp.2016.7472859","title":"Unsupervised user intent modeling by feature-enriched matrix factorization","display_name":"Unsupervised user intent modeling by feature-enriched matrix factorization","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2399691617","doi":"https://doi.org/10.1109/icassp.2016.7472859","mag":"2399691617"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5076610826","display_name":"Yun-Nung Chen","orcid":"https://orcid.org/0000-0003-1777-3942"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yun-Nung Chen","raw_affiliation_strings":["Carnegie Mellon University, School of Computer Science, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, School of Computer Science, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100755311","display_name":"Ming Sun","orcid":"https://orcid.org/0000-0002-5948-2708"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Sun","raw_affiliation_strings":["Carnegie Mellon University, School of Computer Science, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, School of Computer Science, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040668817","display_name":"Alexander I. Rudnicky","orcid":"https://orcid.org/0000-0003-2044-8446"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander I. Rudnicky","raw_affiliation_strings":["Carnegie Mellon University, School of Computer Science, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, School of Computer Science, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002203075","display_name":"Anatole Gershman","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anatole Gershman","raw_affiliation_strings":["Carnegie Mellon University, School of Computer Science, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, School of Computer Science, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076610826"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":2.9993,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.92639321,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2016","issue":null,"first_page":"6150","last_page":"6154"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","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/T12031","display_name":"Speech and dialogue systems","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/T10028","display_name":"Topic Modeling","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/T10203","display_name":"Recommender Systems and Techniques","score":0.9991999864578247,"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":0.879594087600708},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.698946475982666},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6369233727455139},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5634727478027344},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.49689772725105286},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.48937076330184937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4744901657104492},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4548332393169403},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.39804673194885254},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11414104700088501}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.879594087600708},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.698946475982666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6369233727455139},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5634727478027344},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.49689772725105286},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.48937076330184937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4744901657104492},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4548332393169403},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39804673194885254},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11414104700088501},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1598782021","https://openalex.org/W1852412531","https://openalex.org/W1907114407","https://openalex.org/W1936920915","https://openalex.org/W1965355809","https://openalex.org/W1972595521","https://openalex.org/W1987863801","https://openalex.org/W2000157792","https://openalex.org/W2023384780","https://openalex.org/W2043511823","https://openalex.org/W2044910221","https://openalex.org/W2054141820","https://openalex.org/W2067750885","https://openalex.org/W2068547472","https://openalex.org/W2086342113","https://openalex.org/W2093390569","https://openalex.org/W2098880560","https://openalex.org/W2127838323","https://openalex.org/W2135001774","https://openalex.org/W2135441393","https://openalex.org/W2140310134","https://openalex.org/W2144730813","https://openalex.org/W2151048449","https://openalex.org/W2153501885","https://openalex.org/W2153579005","https://openalex.org/W2189149111","https://openalex.org/W2250869925","https://openalex.org/W2252022287","https://openalex.org/W2274861244","https://openalex.org/W2294685269","https://openalex.org/W2397801432","https://openalex.org/W2407905223","https://openalex.org/W2952982889","https://openalex.org/W4206765718","https://openalex.org/W4236146727","https://openalex.org/W4241635929","https://openalex.org/W4294170691","https://openalex.org/W6638754787","https://openalex.org/W6639655227","https://openalex.org/W6640440542","https://openalex.org/W6679900766","https://openalex.org/W6680830989","https://openalex.org/W6682141183","https://openalex.org/W6687170435","https://openalex.org/W6691332274","https://openalex.org/W6764428254","https://openalex.org/W6817586769"],"related_works":["https://openalex.org/W2787993192","https://openalex.org/W2158269427","https://openalex.org/W4381280689","https://openalex.org/W2847365777","https://openalex.org/W3147584709","https://openalex.org/W3128025644","https://openalex.org/W2355048207","https://openalex.org/W2750422482","https://openalex.org/W3125827053","https://openalex.org/W2920521957"],"abstract_inverted_index":{"Spoken":[0],"language":[1],"interfaces":[2],"are":[3],"being":[4],"incorporated":[5,126],"into":[6],"various":[7],"devices":[8],"such":[9],"as":[10],"smart":[11],"phones":[12],"and":[13,69,82,96,111,145],"TVs.":[14],"However,":[15],"dialogue":[16],"systems":[17],"may":[18],"fail":[19],"to":[20,43,52,58,71,76,87],"respond":[21],"correctly":[22],"when":[23],"users'":[24,59,97],"request":[25],"functionality":[26],"is":[27,85],"not":[28],"supported":[29],"by":[30],"currently":[31],"installed":[32],"apps.":[33],"This":[34],"paper":[35],"proposes":[36],"a":[37,50,101],"feature-enriched":[38],"matrix":[39],"factorization":[40],"(MF)":[41],"approach":[42],"model":[44,88],"open":[45],"domain":[46],"intents,":[47],"which":[48],"allows":[49],"system":[51],"dynamically":[53],"add":[54],"unexplored":[55],"domains":[56],"according":[57],"requests.":[60],"First":[61],"we":[62],"leverage":[63],"the":[64,122],"structured":[65],"knowledge":[66],"from":[67],"Wikipedia":[68],"Freebase":[70],"automatically":[72,89],"acquire":[73],"domain-related":[74],"semantics":[75],"enrich":[77],"features":[78,129],"of":[79,116,137],"input":[80],"utterances,":[81],"then":[83],"MF":[84,124],"applied":[86],"acquired":[90],"knowledge,":[91],"published":[92],"app":[93],"textual":[94],"descriptions":[95],"spoken":[98],"requests":[99],"in":[100],"joint":[102],"fashion;":[103],"this":[104],"generates":[105],"latent":[106],"feature":[107],"vectors":[108],"for":[109,142],"utterances":[110],"user":[112],"intents":[113],"without":[114],"need":[115],"prior":[117],"annotations.":[118],"Experiments":[119],"show":[120],"that":[121],"proposed":[123],"models":[125],"with":[127],"rich":[128],"significantly":[130],"improve":[131],"intent":[132],"prediction,":[133],"achieving":[134],"about":[135],"34%":[136],"mean":[138],"average":[139],"precision":[140],"(MAP)":[141],"both":[143],"ASR":[144],"manual":[146],"transcripts.":[147]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}