{"id":"https://openalex.org/W1964812476","doi":"https://doi.org/10.1109/taslp.2014.2303296","title":"Application of Deep Belief Networks for Natural Language Understanding","display_name":"Application of Deep Belief Networks for Natural Language Understanding","publication_year":2014,"publication_date":"2014-02-11","ids":{"openalex":"https://openalex.org/W1964812476","doi":"https://doi.org/10.1109/taslp.2014.2303296","mag":"1964812476"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2014.2303296","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2014.2303296","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-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/A5072875068","display_name":"Ruhi Sarikaya","orcid":"https://orcid.org/0000-0003-2676-2831"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruhi Sarikaya","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA","Microsoft Corporation Redmond,WA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Corporation Redmond,WA,USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108093963","display_name":"Geoffrey E. Hinton","orcid":null},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Geoffrey E. Hinton","raw_affiliation_strings":["Department of Computer Science, University of Toronto, Toronto, ON, Canada","DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF TORONTO, TORONTO, ON, CANADA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Toronto, Toronto, ON, Canada","institution_ids":["https://openalex.org/I185261750"]},{"raw_affiliation_string":"DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF TORONTO, TORONTO, ON, CANADA","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000621526","display_name":"Anoop Deoras","orcid":"https://orcid.org/0009-0007-4566-8767"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anoop Deoras","raw_affiliation_strings":["Microsoft Corporation, Redmond, WA, USA","Microsoft Corporation Redmond,WA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Corporation Redmond,WA,USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":33.0199,"has_fulltext":false,"cited_by_count":460,"citation_normalized_percentile":{"value":0.99848335,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"22","issue":"4","first_page":"778","last_page":"784"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9984999895095825,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/deep-belief-network","display_name":"Deep belief network","score":0.8447284698486328},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7681725025177002},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7457072138786316},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.637046217918396},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6217421293258667},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5720446705818176},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5612403750419617},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5065311193466187},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.47142356634140015},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.44227373600006104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4248453676700592}],"concepts":[{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.8447284698486328},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7681725025177002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7457072138786316},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.637046217918396},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6217421293258667},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5720446705818176},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5612403750419617},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5065311193466187},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.47142356634140015},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.44227373600006104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4248453676700592}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/taslp.2014.2303296","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2014.2303296","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.641.7629","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.641.7629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.toronto.edu/~hinton/absps/ruhijournal.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W44815768","https://openalex.org/W49452484","https://openalex.org/W648947103","https://openalex.org/W1550863320","https://openalex.org/W2053463056","https://openalex.org/W2091671846","https://openalex.org/W2103359087","https://openalex.org/W2116064496","https://openalex.org/W2124537004","https://openalex.org/W2124895976","https://openalex.org/W2124914669","https://openalex.org/W2136922672","https://openalex.org/W2138857742","https://openalex.org/W2147880316","https://openalex.org/W2156909104","https://openalex.org/W2160042006","https://openalex.org/W2160842254","https://openalex.org/W2163306339","https://openalex.org/W2163614729","https://openalex.org/W2166293310","https://openalex.org/W2171144711","https://openalex.org/W2595697910","https://openalex.org/W2606098075","https://openalex.org/W2606321545","https://openalex.org/W2999905431","https://openalex.org/W6680300913","https://openalex.org/W6682082992","https://openalex.org/W6683366680","https://openalex.org/W6684017090"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W4239286941","https://openalex.org/W4231274751","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1489772951"],"abstract_inverted_index":{"Applications":[0],"of":[1,13,16,44,55,141],"Deep":[2],"Belief":[3],"Nets":[4],"(DBN)":[5],"to":[6,26,35,74,93,110,138],"various":[7],"problems":[8],"have":[9],"been":[10],"the":[11,53,84,139,142,159],"subject":[12],"a":[14,36,56,76,95,106,131],"number":[15],"recent":[17,42],"studies":[18],"ranging":[19],"from":[20,80],"image":[21],"classification":[22,115,133],"and":[23,83,122,153,175],"speech":[24],"recognition":[25],"audio":[27],"classification.":[28],"In":[29],"this":[30,47,88],"study":[31],"we":[32],"apply":[33],"DBNs":[34,73],"natural":[37],"language":[38],"understanding":[39],"problem.":[40],"The":[41,126],"surge":[43],"activity":[45],"in":[46,168],"area":[48],"was":[49],"largely":[50],"spurred":[51],"by":[52,87],"development":[54],"greedy":[57],"layer-wise":[58],"pretraining":[59],"method":[60],"that":[61,135],"uses":[62],"an":[63],"efficient":[64],"learning":[65],"algorithm":[66],"called":[67],"Contrastive":[68],"Divergence":[69],"(CD).":[70],"CD":[71],"allows":[72],"learn":[75],"multi-layer":[77],"generative":[78],"model":[79,89,129],"unlabeled":[81,148],"data":[82,149],"features":[85,157,161],"discovered":[86],"are":[90],"then":[91],"used":[92,113],"initialize":[94],"feed-forward":[96],"neural":[97,108],"network":[98,109],"which":[99],"is":[100,136],"fine-tuned":[101],"with":[102,158],"backpropagation.":[103],"We":[104],"compare":[105],"DBN-initialized":[107],"three":[111],"widely":[112],"text":[114],"algorithms:":[116],"Support":[117],"Vector":[118],"Machines":[119],"(SVM),":[120],"boosting":[121],"Maximum":[123],"Entropy":[124],"(MaxEnt).":[125],"plain":[127],"DBN-based":[128,155],"gives":[130],"call-routing":[132],"accuracy":[134],"equal":[137],"best":[140],"other":[143],"models.":[144],"However,":[145],"using":[146],"additional":[147],"for":[150],"DBN":[151],"pre-training":[152],"combining":[154],"learned":[156],"original":[160],"provides":[162],"significant":[163],"gains":[164],"over":[165],"SVMs,":[166],"which,":[167],"turn,":[169],"performed":[170],"better":[171],"than":[172],"both":[173],"MaxEnt":[174],"Boosting.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":33},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":58},{"year":2020,"cited_by_count":61},{"year":2019,"cited_by_count":52},{"year":2018,"cited_by_count":65},{"year":2017,"cited_by_count":49},{"year":2016,"cited_by_count":40},{"year":2015,"cited_by_count":23},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
