{"id":"https://openalex.org/W1969116010","doi":"https://doi.org/10.1145/1401890.1401962","title":"Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model","display_name":"Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model","publication_year":2008,"publication_date":"2008-08-24","ids":{"openalex":"https://openalex.org/W1969116010","doi":"https://doi.org/10.1145/1401890.1401962","mag":"1969116010"},"language":"en","primary_location":{"id":"doi:10.1145/1401890.1401962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401962","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th 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/A5060421432","display_name":"Issei Sato","orcid":"https://orcid.org/0000-0002-5066-1435"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Issei Sato","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024749444","display_name":"Minoru Yoshida","orcid":"https://orcid.org/0000-0002-4376-5674"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Minoru Yoshida","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020912760","display_name":"Hiroshi Nakagawa","orcid":"https://orcid.org/0000-0002-3024-9136"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Nakagawa","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060421432"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.05599218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"587","last_page":"595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9980000257492065,"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":0.9980000257492065,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9886000156402588,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7951928377151489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6777244210243225},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6232821345329285},{"id":"https://openalex.org/keywords/wordnet","display_name":"WordNet","score":0.5987423062324524},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4755651354789734},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4683099687099457},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46222081780433655},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.41000980138778687},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.165406733751297}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7951928377151489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6777244210243225},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6232821345329285},{"id":"https://openalex.org/C157659113","wikidata":"https://www.wikidata.org/wiki/Q533822","display_name":"WordNet","level":2,"score":0.5987423062324524},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4755651354789734},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4683099687099457},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46222081780433655},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41000980138778687},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.165406733751297}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1401890.1401962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401962","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W11253727","https://openalex.org/W111266589","https://openalex.org/W173385071","https://openalex.org/W263845233","https://openalex.org/W1582145286","https://openalex.org/W1967465043","https://openalex.org/W1967687583","https://openalex.org/W2000951787","https://openalex.org/W2069429561","https://openalex.org/W2087309226","https://openalex.org/W2091797506","https://openalex.org/W2097266862","https://openalex.org/W2121564430","https://openalex.org/W2139818818","https://openalex.org/W2151967501","https://openalex.org/W2158266063","https://openalex.org/W2160510157","https://openalex.org/W2166776180","https://openalex.org/W2606965029","https://openalex.org/W4230306435","https://openalex.org/W6682569104"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W2967848559","https://openalex.org/W4299831724","https://openalex.org/W4283803360"],"abstract_inverted_index":{"We":[0],"developed":[1,101],"a":[2,19,56,76,112],"model":[3,99,117],"based":[4,125],"on":[5,126],"nonparametric":[6],"Bayesian":[7],"modeling":[8],"for":[9,102],"automatic":[10],"discovery":[11],"of":[12,42,81,107,115,122],"semantic":[13,26],"relationships":[14],"between":[15],"words":[16,29],"taken":[17,54],"from":[18,111],"corpus.":[20,113],"It":[21],"is":[22,53,73],"aimed":[23],"at":[24],"discovering":[25],"knowledge":[27],"about":[28],"in":[30],"particular":[31],"domains,":[32],"which":[33],"has":[34],"become":[35],"increasingly":[36],"important":[37],"with":[38,59],"the":[39,60,63,66,69,88,93,120],"growing":[40],"use":[41],"text":[43],"mining,":[44],"information":[45],"retrieval,":[46],"and":[47,65,92],"speech":[48],"recognition.":[49],"The":[50,79,96],"subject-predicate":[51,108],"structure":[52,58,72,105],"as":[55,62,68,75],"syntactic":[57],"noun":[61],"subject":[64],"verb":[67],"predicate.":[70],"This":[71],"regarded":[74],"graph":[77,83,104,123],"structure.":[78],"generation":[80],"this":[82,103,116],"can":[84],"be":[85],"modeled":[86],"using":[87],"hierarchical":[89],"Dirichlet":[90],"process":[91],"Pitman-Yor":[94],"process.":[95],"probabilistic":[97],"generative":[98],"we":[100],"consists":[106],"structures":[109],"extracted":[110],"Evaluation":[114],"by":[118],"measuring":[119],"performance":[121],"clustering":[124],"WordNet":[127],"similarities":[128],"demonstrated":[129],"that":[130],"it":[131],"outperforms":[132],"other":[133],"baseline":[134],"models.":[135]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}