{"id":"https://openalex.org/W2145153925","doi":"https://doi.org/10.4304/jcp.5.4.654-661","title":"Classifying Documents with Maximum Likelihood Approximation of the Dirichlet Multinomial Gibbs Model","display_name":"Classifying Documents with Maximum Likelihood Approximation of the Dirichlet Multinomial Gibbs Model","publication_year":2010,"publication_date":"2010-04-01","ids":{"openalex":"https://openalex.org/W2145153925","doi":"https://doi.org/10.4304/jcp.5.4.654-661","mag":"2145153925"},"language":"en","primary_location":{"id":"doi:10.4304/jcp.5.4.654-661","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.5.4.654-661","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","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/A5011594122","display_name":"Shibin Zhou","orcid":"https://orcid.org/0000-0002-2808-1928"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shibin Zhou","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089034482","display_name":"Zhao Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao Cao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028008344","display_name":"Yushu Liu","orcid":"https://orcid.org/0000-0003-2014-2092"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yushu Liu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5011594122"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14491718,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.98089998960495,"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":0.98089998960495,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9506000280380249,"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":0.9484000205993652,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.7058809995651245},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.639947235584259},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.5715274810791016},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5621006488800049},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47114649415016174},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.4482354521751404},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3723162114620209},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3667297065258026},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3096889853477478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19807219505310059},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.1379701793193817},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.09419217705726624},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.092988520860672}],"concepts":[{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.7058809995651245},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.639947235584259},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.5715274810791016},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5621006488800049},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47114649415016174},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.4482354521751404},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3723162114620209},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3667297065258026},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3096889853477478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19807219505310059},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.1379701793193817},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.09419217705726624},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.092988520860672},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4304/jcp.5.4.654-661","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.5.4.654-661","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2939843948","https://openalex.org/W2016318053","https://openalex.org/W2352674739","https://openalex.org/W2380850649","https://openalex.org/W1556021157","https://openalex.org/W2143869282","https://openalex.org/W1984724218","https://openalex.org/W2070114007","https://openalex.org/W2891616219","https://openalex.org/W1985440090"],"abstract_inverted_index":{"In":[0,50],"the":[1,4,23,29,37,54,86,91,101,105,136],"text":[2,127],"analysis,":[3],"Dirichlet":[5,72],"compound":[6],"multinomial":[7,31,73],"(DCM)distribution":[8],"has":[9],"recently":[10],"been":[11],"shown":[12],"to":[13,81],"be":[14],"a":[15,40,47,64],"good":[16],"model":[17,76,93,138],"for":[18,53],"documents":[19],"because":[20],"it":[21],"captures":[22],"phenomenon":[24,35],"of":[25,39,56,59,66,90,107,109,135],"word":[26,42],"burstiness,":[27],"unlike":[28],"standard":[30],"distribution.":[32,83],"The":[33],"burstiness":[34],"describes":[36],"behavior":[38],"rare":[41],"appearing":[43],"many":[44],"times":[45],"in":[46],"single":[48],"document.":[49],"this":[51],"paper,":[52],"sake":[55],"improving":[57],"performance":[58],"modeling":[60],"documents,":[61],"we":[62,129],"propose":[63],"variant":[65],"DCM":[67,82,114],"and":[68,113],"Gibbs":[69,74,79,96,110],"distribution":[70,111],"called":[71],"(DMG)":[75],"by":[77,120],"introducing":[78],"parameters":[80],"We":[84],"demonstrate":[85],"maximum":[87,132],"likelihood":[88,133],"procedure":[89],"DMG":[92,102,137],"with":[94],"these":[95],"parameters.":[97],"By":[98],"our":[99,121],"experiments,":[100],"approach":[103],"inherit":[104],"merits":[106],"methods":[108],"approximation":[112,134],"estimation.":[115],"More":[116],"specifically,":[117],"as":[118],"revealed":[119],"experimental":[122],"results":[123],"on":[124],"various":[125],"real-world":[126],"datasets,":[128],"show":[130],"that":[131],"is":[139],"more":[140],"desirable":[141],"than":[142],"some":[143],"current":[144],"state-of-the-art":[145],"methods.":[146]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
