{"id":"https://openalex.org/W1975685775","doi":"https://doi.org/10.3115/1117794.1117799","title":"Topic analysis using a finite mixture model","display_name":"Topic analysis using a finite mixture model","publication_year":2000,"publication_date":"2000-01-01","ids":{"openalex":"https://openalex.org/W1975685775","doi":"https://doi.org/10.3115/1117794.1117799","mag":"1975685775"},"language":"en","primary_location":{"id":"doi:10.3115/1117794.1117799","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1117794.1117799","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=1117799&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics -","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://dl.acm.org/ft_gateway.cfm?id=1117799&type=pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101670173","display_name":"Hang Li","orcid":"https://orcid.org/0000-0002-4684-1951"},"institutions":[{"id":"https://openalex.org/I118347220","display_name":"NEC (Japan)","ror":"https://ror.org/04jndar25","country_code":"JP","type":"company","lineage":["https://openalex.org/I118347220"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hang Li","raw_affiliation_strings":["NEC Corporation","[NEC Corp.]"],"affiliations":[{"raw_affiliation_string":"NEC Corporation","institution_ids":[]},{"raw_affiliation_string":"[NEC Corp.]","institution_ids":["https://openalex.org/I118347220"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021981442","display_name":"Kenji Yamanishi","orcid":"https://orcid.org/0000-0001-7370-9991"},"institutions":[{"id":"https://openalex.org/I118347220","display_name":"NEC (Japan)","ror":"https://ror.org/04jndar25","country_code":"JP","type":"company","lineage":["https://openalex.org/I118347220"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kenji Yamanishi","raw_affiliation_strings":["NEC Corporation","[NEC Corp.]"],"affiliations":[{"raw_affiliation_string":"NEC Corporation","institution_ids":[]},{"raw_affiliation_string":"[NEC Corp.]","institution_ids":["https://openalex.org/I118347220"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101670173"],"corresponding_institution_ids":["https://openalex.org/I118347220"],"apc_list":null,"apc_paid":null,"fwci":1.5914,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.8616651,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"13","issue":null,"first_page":"35","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9991000294685364,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9991000294685364,"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":0.9962999820709229,"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"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9944999814033508,"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":0.7808355093002319},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.6661204099655151},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6269096732139587},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5959057807922363},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5845326781272888},{"id":"https://openalex.org/keywords/statistical-analysis","display_name":"Statistical analysis","score":0.42364779114723206},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33805108070373535},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13131380081176758},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09735739231109619}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7808355093002319},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.6661204099655151},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6269096732139587},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5959057807922363},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5845326781272888},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.42364779114723206},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33805108070373535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13131380081176758},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09735739231109619},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3115/1117794.1117799","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1117794.1117799","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=1117799&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics -","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.11.6106","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.6106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://research.microsoft.com/users/hangli/HP_files/Li-Yamanishi-IPM03-Org.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.13.5670","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.5670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://acl.ldc.upenn.edu/W/W00/W00-1305.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.83.9655","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.9655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.scils.rutgers.edu/~muresan/IR/Docs/Articles/ipmLi2003.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/1117794.1117799","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1117794.1117799","pdf_url":"http://dl.acm.org/ft_gateway.cfm?id=1117799&type=pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics -","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.75,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1975685775.pdf","grobid_xml":"https://content.openalex.org/works/W1975685775.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W285471286","https://openalex.org/W1553187123","https://openalex.org/W1557074680","https://openalex.org/W1828401780","https://openalex.org/W2013657661","https://openalex.org/W2049633694","https://openalex.org/W2060216474","https://openalex.org/W2062847911","https://openalex.org/W2068782468","https://openalex.org/W2075006521","https://openalex.org/W2076800308","https://openalex.org/W2097089247","https://openalex.org/W2099111195","https://openalex.org/W2107743791","https://openalex.org/W2111705563","https://openalex.org/W2121227244","https://openalex.org/W2121407732","https://openalex.org/W2135909747","https://openalex.org/W2149684865","https://openalex.org/W2161793958","https://openalex.org/W2167055684","https://openalex.org/W2788453169","https://openalex.org/W4233135949"],"related_works":["https://openalex.org/W3200230513","https://openalex.org/W2769501189","https://openalex.org/W2226452791","https://openalex.org/W2962686197","https://openalex.org/W4312773271","https://openalex.org/W1569283511","https://openalex.org/W2132052677","https://openalex.org/W2888805565","https://openalex.org/W4315588616","https://openalex.org/W2296205523"],"abstract_inverted_index":{"We":[0,31,46],"address":[1],"the":[2,29],"issue":[3],"of":[4,51],"'topic":[5],"analysis,'":[6],"by":[7,49],"which":[8,15],"is":[9],"determined":[10],"a":[11,22,33,56,62,66,77],"text's":[12],"topic":[13],"structure,":[14],"indicates":[16],"what":[17],"topics":[18,26,48],"are":[19],"included":[20],"in":[21],"text,":[23],"and":[24,44,54],"how":[25],"change":[27],"within":[28,65],"text.":[30,67],"propose":[32],"novel":[34],"approach":[35],"to":[36,60],"this":[37],"issue,":[38],"one":[39],"based":[40],"on":[41],"statistical":[42],"modeling":[43],"learning.":[45],"represent":[47,61],"means":[50],"word":[52,63],"clusters,":[53],"employ":[55],"finite":[57],"mixture":[58],"model":[59],"distribution":[64],"Our":[68],"experimental":[69],"results":[70],"indicate":[71],"that":[72,79],"our":[73],"method":[74,78],"significantly":[75],"outperforms":[76],"combines":[80],"existing":[81],"techniques.":[82]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}