{"id":"https://openalex.org/W2104676427","doi":"https://doi.org/10.1145/2339530.2339549","title":"The contextual focused topic model","display_name":"The contextual focused topic model","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2104676427","doi":"https://doi.org/10.1145/2339530.2339549","mag":"2104676427"},"language":"en","primary_location":{"id":"doi:10.1145/2339530.2339549","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5100647360","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0003-0003-0210"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010394308","display_name":"Mingyuan Zhou","orcid":"https://orcid.org/0000-0002-4253-2780"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingyuan Zhou","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016448581","display_name":"Lawrence Carin","orcid":"https://orcid.org/0000-0001-6277-7948"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Carin","raw_affiliation_strings":["Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100647360"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":9.7238,"has_fulltext":false,"cited_by_count":55,"citation_normalized_percentile":{"value":0.9801673,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"96","last_page":"104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9969000220298767,"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/T10028","display_name":"Topic Modeling","score":0.9969000220298767,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8094013333320618},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.6872252821922302},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.6729018688201904},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.647512674331665},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6241147518157959},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6062929034233093},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49612244963645935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4911474883556366},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.48930975794792175},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4682040810585022},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.43947505950927734},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43434906005859375},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.40336233377456665},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3870507478713989},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35185155272483826},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.34785228967666626}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8094013333320618},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.6872252821922302},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.6729018688201904},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.647512674331665},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6241147518157959},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6062929034233093},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49612244963645935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4911474883556366},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.48930975794792175},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4682040810585022},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.43947505950927734},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43434906005859375},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40336233377456665},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3870507478713989},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35185155272483826},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34785228967666626},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2339530.2339549","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.388.6638","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.388.6638","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://people.ee.duke.edu/~lcarin/Xu_KDD_2012.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1517266559","https://openalex.org/W1680797894","https://openalex.org/W1880262756","https://openalex.org/W1991327379","https://openalex.org/W1998436802","https://openalex.org/W2001082470","https://openalex.org/W2022322548","https://openalex.org/W2066636486","https://openalex.org/W2069429561","https://openalex.org/W2092883204","https://openalex.org/W2107743791","https://openalex.org/W2108346334","https://openalex.org/W2109154616","https://openalex.org/W2112104211","https://openalex.org/W2114727905","https://openalex.org/W2126184790","https://openalex.org/W2128002512","https://openalex.org/W2135029798","https://openalex.org/W2138621811","https://openalex.org/W2139823104","https://openalex.org/W2149288670","https://openalex.org/W2150461699","https://openalex.org/W2151967501","https://openalex.org/W2154455818","https://openalex.org/W2157339239","https://openalex.org/W2158266063","https://openalex.org/W2165599843","https://openalex.org/W2165636119","https://openalex.org/W2167810193","https://openalex.org/W2168084958","https://openalex.org/W2216511711","https://openalex.org/W2263034332","https://openalex.org/W2506541212","https://openalex.org/W2947000318","https://openalex.org/W3140968660","https://openalex.org/W4233135949","https://openalex.org/W6677317845","https://openalex.org/W6680012447","https://openalex.org/W6681689991","https://openalex.org/W6682494755","https://openalex.org/W6682569104","https://openalex.org/W6684489972","https://openalex.org/W7048060829"],"related_works":["https://openalex.org/W2769501189","https://openalex.org/W4315588616","https://openalex.org/W4312773271","https://openalex.org/W2888805565","https://openalex.org/W2962686197","https://openalex.org/W2207653751","https://openalex.org/W3005513013","https://openalex.org/W2611137333","https://openalex.org/W4317422773","https://openalex.org/W4291700620"],"abstract_inverted_index":{"A":[0],"nonparametric":[1],"Bayesian":[2],"contextual":[3,25],"focused":[4,47],"topic":[5],"model":[6],"(cFTM)":[7],"is":[8,42,60,107,155],"proposed.":[9],"The":[10,33,114],"cFTM":[11,115],"infers":[12,117],"a":[13,39,69,149],"sparse":[14],"(\"focused\")":[15],"set":[16,48],"of":[17,49,120,129,138],"topics":[18,50,66,80,121],"for":[19,86,148,163],"each":[20,53],"document,":[21],"while":[22],"also":[23,61],"leveraging":[24],"information":[26,144],"about":[27],"the":[28,46,57,87,104,118,125,127,135,139],"author(s)":[29,89],"and":[30,55,90,96,102,112,131,134,142,159],"document":[31,71],"venue.":[32,91],"hierarchical":[34],"beta":[35],"process,":[36,41],"coupled":[37],"with":[38,52,68,170],"Bernoulli":[40],"employed":[43,62],"to":[44,63,79,109,123,172],"infer":[45,64,97],"associated":[51,67,88],"author":[54,130],"venue;":[56],"same":[58],"construction":[59],"those":[65],"given":[70,150],"that":[72,81],"are":[73,82,161],"unusual":[74],"(termed":[75],"\"random":[76],"effects\"),":[77],"relative":[78],"inferred":[83],"as":[84],"probable":[85],"To":[92],"leverage":[93],"statistical":[94],"strength":[95],"latent":[98],"interrelationships":[99],"between":[100],"authors":[101,111],"venues,":[103],"Dirichlet":[105],"process":[106],"utilized":[108],"cluster":[110],"venues.":[113],"automatically":[116],"number":[119,128],"needed":[122],"represent":[124],"corpus,":[126],"venue":[132,141],"clusters,":[133],"probabilistic":[136],"importance":[137],"author,":[140],"random-effect":[143],"on":[145],"word":[146],"assignment":[147],"document.":[151],"Efficient":[152],"MCMC":[153],"inference":[154],"presented.":[156],"Example":[157],"results":[158],"interpretations":[160],"presented":[162],"two":[164],"real":[165],"datasets,":[166],"demonstrating":[167],"promising":[168],"performance,":[169],"comparison":[171],"other":[173],"state-of-the-art":[174],"methods.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":9}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}