{"id":"https://openalex.org/W1967109167","doi":"https://doi.org/10.1109/asru.2007.4430099","title":"Refine bigram PLSA model by assigning latent topics unevenly","display_name":"Refine bigram PLSA model by assigning latent topics unevenly","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W1967109167","doi":"https://doi.org/10.1109/asru.2007.4430099","mag":"1967109167"},"language":"en","primary_location":{"id":"doi:10.1109/asru.2007.4430099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2007.4430099","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","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/A5077127148","display_name":"Jiazhong Nie","orcid":null},"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"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiazhong Nie","raw_affiliation_strings":["State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","Peking Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking Univ., Beijing","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014723423","display_name":"Runxin Li","orcid":null},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runxin Li","raw_affiliation_strings":["State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","Peking Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking Univ., Beijing","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004014436","display_name":"Dingsheng Luo","orcid":"https://orcid.org/0000-0001-6997-3984"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dingsheng Luo","raw_affiliation_strings":["State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","Peking Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking Univ., Beijing","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111110989","display_name":"Xihong Wu","orcid":null},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xihong Wu","raw_affiliation_strings":["State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","Peking Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Machine Perception and Speech & Hearing Research State Key Laboratory of Machine Perception and Speech & Hearing Research Center, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking Univ., Beijing","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077127148"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":1.877,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.87160609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9998000264167786,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9998000264167786,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9990000128746033,"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/bigram","display_name":"Bigram","score":0.9898111820220947},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.8768994808197021},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.861137866973877},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8118205666542053},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6634469032287598},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6012243628501892},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5920358300209045},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5706290602684021},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5002017021179199},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.48051485419273376},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.45006975531578064},{"id":"https://openalex.org/keywords/distributional-semantics","display_name":"Distributional semantics","score":0.4448944926261902},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3340698778629303},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.11174693703651428},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.07234781980514526}],"concepts":[{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.9898111820220947},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.8768994808197021},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.861137866973877},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8118205666542053},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6634469032287598},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6012243628501892},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5920358300209045},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5706290602684021},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5002017021179199},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.48051485419273376},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.45006975531578064},{"id":"https://openalex.org/C2778828372","wikidata":"https://www.wikidata.org/wiki/Q5283209","display_name":"Distributional semantics","level":3,"score":0.4448944926261902},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3340698778629303},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.11174693703651428},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.07234781980514526},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C137546455","wikidata":"https://www.wikidata.org/wiki/Q3213474","display_name":"Trigram","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asru.2007.4430099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2007.4430099","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W85801758","https://openalex.org/W1612003148","https://openalex.org/W1880262756","https://openalex.org/W1983578042","https://openalex.org/W2097927681","https://openalex.org/W2099345940","https://openalex.org/W2103189252","https://openalex.org/W2104210067","https://openalex.org/W2107743791","https://openalex.org/W2112971401","https://openalex.org/W2950186769","https://openalex.org/W4233135949","https://openalex.org/W4237791300","https://openalex.org/W4252434862","https://openalex.org/W6636440780","https://openalex.org/W6676893574"],"related_works":["https://openalex.org/W2041167939","https://openalex.org/W167435909","https://openalex.org/W2056250865","https://openalex.org/W1700330385","https://openalex.org/W1903115690","https://openalex.org/W2397861987","https://openalex.org/W2595298885","https://openalex.org/W1603105762","https://openalex.org/W2131111393","https://openalex.org/W2223833155"],"abstract_inverted_index":{"As":[0,109],"an":[1,102],"important":[2],"component":[3],"in":[4],"many":[5],"speech":[6],"and":[7,31,86,131],"language":[8,12,41],"processing":[9],"applications,":[10],"statistical":[11],"model":[13,30,49,116],"has":[14],"been":[15],"widely":[16],"investigated.":[17],"The":[18],"bigram":[19,47,79,83,114,145],"topic":[20,33,48,53,80],"model,":[21,34,81,85],"which":[22],"combines":[23],"advantages":[24],"of":[25,72,104,142],"both":[26],"the":[27,32,45,51,61,69,82,126,140,143],"traditional":[28],"n-gram":[29],"turns":[35],"out":[36],"to":[37,68,97,101],"be":[38],"a":[39,77,88,110,112],"promising":[40],"modeling":[42],"approach.":[43],"However,":[44],"original":[46],"assigns":[50,94],"same":[52],"number":[54],"for":[55],"each":[56],"context":[57,73,98],"word":[58],"but":[59],"ignores":[60],"fact":[62],"that":[63,92],"there":[64],"are":[65,137],"different":[66],"complexities":[67],"latent":[70,95,106],"semantics":[71],"words.":[74],"we":[75],"present":[76],"new":[78],"PLSA":[84,115,146],"propose":[87],"modified":[89],"training":[90],"strategy":[91],"unevenly":[93],"topics":[96],"words":[99],"according":[100],"estimation":[103],"their":[105],"semantic":[107],"complexities.":[108],"consequence,":[111],"refined":[113,144],"is":[117],"reached.":[118],"Experiments":[119],"on":[120,135],"HUB4":[121],"Mandarin":[122],"test":[123],"transcriptions":[124],"reveal":[125],"superiority":[127],"over":[128],"existing":[129],"models":[130],"further":[132],"performance":[133],"improvements":[134],"perplexity":[136],"achieved":[138],"through":[139],"use":[141],"model.":[147]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}