{"id":"https://openalex.org/W2773498225","doi":"https://doi.org/10.1109/tkde.2017.2778720","title":"Topic Models for Unsupervised Cluster Matching","display_name":"Topic Models for Unsupervised Cluster Matching","publication_year":2017,"publication_date":"2017-11-30","ids":{"openalex":"https://openalex.org/W2773498225","doi":"https://doi.org/10.1109/tkde.2017.2778720","mag":"2773498225"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2017.2778720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2017.2778720","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Knowledge and Data Engineering","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/A5034538103","display_name":"Tomoharu Iwata","orcid":"https://orcid.org/0000-0003-4425-1971"},"institutions":[{"id":"https://openalex.org/I46980622","display_name":"Kyoto Seika University","ror":"https://ror.org/05g4f0342","country_code":"JP","type":"education","lineage":["https://openalex.org/I46980622"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoharu Iwata","raw_affiliation_strings":["NTT Communication Science Laboratories, Seikacho, Kyoto, Japan","[NTT Communication Science Laboratories, Seikacho, Kyoto, Japan]"],"raw_orcid":"https://orcid.org/0000-0003-4425-1971","affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Seikacho, Kyoto, Japan","institution_ids":["https://openalex.org/I46980622"]},{"raw_affiliation_string":"[NTT Communication Science Laboratories, Seikacho, Kyoto, Japan]","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110220773","display_name":"Tsutomu Hirao","orcid":null},"institutions":[{"id":"https://openalex.org/I46980622","display_name":"Kyoto Seika University","ror":"https://ror.org/05g4f0342","country_code":"JP","type":"education","lineage":["https://openalex.org/I46980622"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tsutomu Hirao","raw_affiliation_strings":["NTT Communication Science Laboratories, Seikacho, Kyoto, Japan","[NTT Communication Science Laboratories, Seikacho, Kyoto, Japan]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Seikacho, Kyoto, Japan","institution_ids":["https://openalex.org/I46980622"]},{"raw_affiliation_string":"[NTT Communication Science Laboratories, Seikacho, Kyoto, Japan]","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105268757","display_name":"Naonori Ueda","orcid":null},"institutions":[{"id":"https://openalex.org/I46980622","display_name":"Kyoto Seika University","ror":"https://ror.org/05g4f0342","country_code":"JP","type":"education","lineage":["https://openalex.org/I46980622"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Naonori Ueda","raw_affiliation_strings":["NTT Communication Science Laboratories, Seikacho, Kyoto, Japan","[NTT Communication Science Laboratories, Seikacho, Kyoto, Japan]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Seikacho, Kyoto, Japan","institution_ids":["https://openalex.org/I46980622"]},{"raw_affiliation_string":"[NTT Communication Science Laboratories, Seikacho, Kyoto, Japan]","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8588,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.89587019,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"30","issue":"4","first_page":"786","last_page":"795"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9955000281333923,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9955000281333923,"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":0.9872000217437744,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8420339226722717},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.660868227481842},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6175372004508972},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5777203440666199},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5722125172615051},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.56572425365448},{"id":"https://openalex.org/keywords/vector-space-model","display_name":"Vector space model","score":0.5161255598068237},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5086323022842407},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.448982298374176},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4395443797111511},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.41686946153640747},{"id":"https://openalex.org/keywords/vector-space","display_name":"Vector space","score":0.41070204973220825},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3918485641479492},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.34406328201293945},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09905633330345154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8420339226722717},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.660868227481842},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6175372004508972},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5777203440666199},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5722125172615051},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.56572425365448},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.5161255598068237},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5086323022842407},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.448982298374176},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4395443797111511},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.41686946153640747},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.41070204973220825},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3918485641479492},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.34406328201293945},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09905633330345154},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2017.2778720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2017.2778720","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W77953724","https://openalex.org/W133569503","https://openalex.org/W1556255569","https://openalex.org/W1581799170","https://openalex.org/W1612003148","https://openalex.org/W1625255723","https://openalex.org/W1880262756","https://openalex.org/W1946137962","https://openalex.org/W1958912078","https://openalex.org/W1985874279","https://openalex.org/W2001082470","https://openalex.org/W2009237467","https://openalex.org/W2018605660","https://openalex.org/W2020842694","https://openalex.org/W2033593667","https://openalex.org/W2037668034","https://openalex.org/W2062955551","https://openalex.org/W2073471108","https://openalex.org/W2111068739","https://openalex.org/W2115707882","https://openalex.org/W2118338035","https://openalex.org/W2129004009","https://openalex.org/W2138047313","https://openalex.org/W2140406733","https://openalex.org/W2151967501","https://openalex.org/W2158266063","https://openalex.org/W2209098276","https://openalex.org/W2222512263","https://openalex.org/W2261463483","https://openalex.org/W2293888570","https://openalex.org/W2962684168","https://openalex.org/W4230502578","https://openalex.org/W4231510805","https://openalex.org/W4234189512","https://openalex.org/W4248892431","https://openalex.org/W6603184878","https://openalex.org/W6605405451","https://openalex.org/W6634688493","https://openalex.org/W6636440780","https://openalex.org/W6636494156","https://openalex.org/W6639619044","https://openalex.org/W6676692661","https://openalex.org/W6677448297","https://openalex.org/W6680649563","https://openalex.org/W6680859986","https://openalex.org/W6682569104","https://openalex.org/W6688112818"],"related_works":["https://openalex.org/W2160402959","https://openalex.org/W1500125366","https://openalex.org/W2352674739","https://openalex.org/W4309228610","https://openalex.org/W4248157169","https://openalex.org/W2096728994","https://openalex.org/W2783657965","https://openalex.org/W2551770406","https://openalex.org/W1557970708","https://openalex.org/W2137763598"],"abstract_inverted_index":{"We":[0,138],"propose":[1],"topic":[2,59,68,74,90,100,124],"models":[3],"for":[4,104,144],"unsupervised":[5],"cluster":[6,119,132],"matching,":[7],"which":[8],"is":[9,77,84,120,159],"the":[10,25,89,130,145,156],"task":[11],"of":[12,67,88,155,168],"finding":[13],"matching":[14],"between":[15,30],"clusters":[16,32],"in":[17,33,52,71,111],"different":[18,112],"domains":[19],"without":[20,37],"correspondence":[21,29],"information.":[22],"For":[23],"example,":[24],"proposed":[26,47,146,157],"model":[27,48,147,158],"finds":[28],"document":[31,83],"English":[34],"and":[35,43,61,93,170],"German":[36],"alignment":[38],"information,":[39],"such":[40],"as":[41],"dictionaries":[42],"parallel":[44],"sentences/documents.":[45],"The":[46,153],"assumes":[49],"that":[50,76],"documents":[51,110],"all":[53,80],"languages":[54,113],"have":[55],"a":[56,72,99,123],"common":[57,115],"latent":[58,73],"structure,":[60],"there":[62],"are":[63,133],"potentially":[64],"infinite":[65],"number":[66],"proportion":[69,91,101,125],"vectors":[70,92],"space":[75],"shared":[78],"by":[79],"languages.":[81],"Each":[82],"generated":[85],"using":[86],"one":[87],"language-specific":[94],"word":[95],"distributions.":[96],"By":[97],"inferring":[98],"vector":[102],"used":[103],"each":[105,118],"document,":[106],"we":[107],"can":[108],"allocate":[109],"into":[114,129],"clusters,":[116],"where":[117],"associated":[121],"with":[122,161],"vector.":[126],"Documents":[127],"assigned":[128],"same":[131],"considered":[134],"to":[135],"be":[136],"matched.":[137],"develop":[139],"an":[140],"efficient":[141],"inference":[142],"procedure":[143],"based":[148],"on":[149],"collapsed":[150],"Gibbs":[151],"sampling.":[152],"effectiveness":[154],"demonstrated":[160],"real":[162],"data":[163],"sets":[164],"including":[165],"multilingual":[166],"corpora":[167],"Wikipedia":[169],"product":[171],"reviews.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}