{"id":"https://openalex.org/W2170772554","doi":"https://doi.org/10.1145/1645953.1645997","title":"An integrated discriminative probabilistic approach to information extraction","display_name":"An integrated discriminative probabilistic approach to information extraction","publication_year":2009,"publication_date":"2009-11-02","ids":{"openalex":"https://openalex.org/W2170772554","doi":"https://doi.org/10.1145/1645953.1645997","mag":"2170772554"},"language":"en","primary_location":{"id":"doi:10.1145/1645953.1645997","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1645953.1645997","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 conference on Information and knowledge management","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/A5101593848","display_name":"Xiaofeng Yu","orcid":"https://orcid.org/0000-0002-6770-7532"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaofeng Yu","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018582154","display_name":"Wai Lam","orcid":"https://orcid.org/0000-0001-5479-377X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wai Lam","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100427253","display_name":"Bo Chen","orcid":"https://orcid.org/0000-0001-5151-9388"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Chen","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101593848"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":1.7447,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.88015096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"325","last_page":"334"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9988999962806702,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.7676571607589722},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7584974765777588},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7110996842384338},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6773478984832764},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5512229204177856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5211727023124695},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5048471093177795},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.49826502799987793},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46779710054397583},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4488813281059265},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.42664772272109985},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4211593270301819},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41059473156929016},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3760263919830322}],"concepts":[{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.7676571607589722},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7584974765777588},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7110996842384338},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6773478984832764},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5512229204177856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5211727023124695},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5048471093177795},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.49826502799987793},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46779710054397583},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4488813281059265},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.42664772272109985},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4211593270301819},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41059473156929016},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3760263919830322}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1645953.1645997","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1645953.1645997","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 conference on Information and knowledge management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.75,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W179282915","https://openalex.org/W203049729","https://openalex.org/W1547546052","https://openalex.org/W1581262234","https://openalex.org/W1599188306","https://openalex.org/W1766290689","https://openalex.org/W1774330103","https://openalex.org/W1977970897","https://openalex.org/W1986398135","https://openalex.org/W2012170877","https://openalex.org/W2024060531","https://openalex.org/W2056760934","https://openalex.org/W2084051155","https://openalex.org/W2096765155","https://openalex.org/W2129712609","https://openalex.org/W2138309709","https://openalex.org/W2147880316","https://openalex.org/W2152520640","https://openalex.org/W2158188757","https://openalex.org/W2167138081","https://openalex.org/W4256238177"],"related_works":["https://openalex.org/W3087071515","https://openalex.org/W4283077537","https://openalex.org/W2999603699","https://openalex.org/W2464065341","https://openalex.org/W2947536360","https://openalex.org/W3086697448","https://openalex.org/W2968689489","https://openalex.org/W4302573481","https://openalex.org/W2054737069","https://openalex.org/W2505308168"],"abstract_inverted_index":{"Probabilistic":[0],"graphical":[1,134],"models":[2],"for":[3,59,107,150,162],"sequence":[4,83],"data":[5],"enable":[6],"us":[7],"to":[8,111],"effectively":[9],"deal":[10],"with":[11],"inherent":[12],"uncertainty":[13,45,64],"in":[14,47,81,89,159],"many":[15],"real-world":[16],"domains.":[17],"However,":[18],"they":[19],"operate":[20],"on":[21,28,170],"a":[22,35,100,115,139],"mostly":[23],"propositional":[24],"level.":[25],"Logic":[26],"approaches,":[27],"the":[29,98,113,120,171,179],"other":[30],"hand,":[31],"can":[32],"compactly":[33],"represent":[34],"wide":[36],"variety":[37],"of":[38,86,118,122,142,183],"knowledge,":[39],"especially":[40],"first-order":[41,54],"ones,":[42],"but":[43],"treat":[44],"only":[46],"limited":[48],"ways.":[49],"Therefore,":[50],"combining":[51],"probability":[52],"and":[53,70,84,136,146,156,181],"logic":[55],"is":[56],"highly":[57],"desirable":[58],"information":[60,175],"extraction":[61,152,176],"which":[62],"requires":[63],"modeling":[65],"as":[66,68],"well":[67],"dependency":[69],"deeper":[71],"knowledge":[72],"representation.":[73],"In":[74],"this":[75,123],"paper,":[76],"we":[77],"model":[78,127],"both":[79],"segmentations":[80],"observation":[82],"relations":[85,145],"segments":[87],"simultaneously":[88],"our":[90,184],"proposed":[91],"integrated":[92,126],"discriminative":[93],"probabilistic":[94,133],"framework.":[95],"We":[96,166],"propose":[97],"Metropolis-Hastings,":[99],"Markov":[101],"chain":[102],"Monte":[103],"Carlo":[104],"(MCMC)":[105],"algorithm":[106],"approximate":[108],"Bayesian":[109],"inference":[110],"find":[112],"maximum":[114],"posteriori":[116],"assignment":[117],"all":[119],"variables":[121],"model.":[124],"This":[125],"has":[128],"several":[129],"advantages":[130],"over":[131],"previous":[132],"models,":[135],"it":[137],"offers":[138],"great":[140],"capability":[141],"extracting":[143],"implicit":[144],"new":[147],"relation":[148,151],"discovery":[149],"from":[153],"encyclopedic":[154],"documents,":[155],"capturing":[157],"sub-structures":[158],"named":[160,163],"entities":[161],"entity":[164],"recognition.":[165],"performed":[167],"extensive":[168],"experiments":[169],"above":[172],"two":[173],"well-established":[174],"tasks,":[177],"illustrating":[178],"feasibility":[180],"promise":[182],"approach.":[185]},"counts_by_year":[{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}