{"id":"https://openalex.org/W2251421040","doi":"https://doi.org/10.3115/v1/p14-1077","title":"Encoding Relation Requirements for Relation Extraction via Joint Inference","display_name":"Encoding Relation Requirements for Relation Extraction via Joint Inference","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2251421040","doi":"https://doi.org/10.3115/v1/p14-1077","mag":"2251421040"},"language":"en","primary_location":{"id":"doi:10.3115/v1/p14-1077","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/p14-1077","pdf_url":"https://aclanthology.org/P14-1077.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/P14-1077.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100625784","display_name":"Liwei Chen","orcid":"https://orcid.org/0000-0002-9858-0765"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liwei Chen","raw_affiliation_strings":["ICST, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"ICST, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102220317","display_name":"Yansong Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yansong Feng","raw_affiliation_strings":["ICST, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"ICST, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047856952","display_name":"Songfang Huang","orcid":"https://orcid.org/0000-0001-8084-0904"},"institutions":[{"id":"https://openalex.org/I4210126794","display_name":"IBM Research (China)","ror":"https://ror.org/02yg1pf55","country_code":"CN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126794"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songfang Huang","raw_affiliation_strings":["IBM China Research Lab, Beijing, China"],"affiliations":[{"raw_affiliation_string":"IBM China Research Lab, Beijing, China","institution_ids":["https://openalex.org/I4210126794"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101495468","display_name":"Yong Qin","orcid":"https://orcid.org/0009-0003-1460-3574"},"institutions":[{"id":"https://openalex.org/I4210126794","display_name":"IBM Research (China)","ror":"https://ror.org/02yg1pf55","country_code":"CN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126794"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Qin","raw_affiliation_strings":["IBM China Research Lab, Beijing, China"],"affiliations":[{"raw_affiliation_string":"IBM China Research Lab, Beijing, China","institution_ids":["https://openalex.org/I4210126794"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037132097","display_name":"Dongyan Zhao","orcid":"https://orcid.org/0000-0002-0396-6703"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyan Zhao","raw_affiliation_strings":["ICST, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"ICST, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100625784"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.1143,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.9014009,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"818","last_page":"827"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9990000128746033,"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/T11719","display_name":"Data Quality and Management","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8025393486022949},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7915583848953247},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7453123927116394},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7408262491226196},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7271828651428223},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.7167300581932068},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5952044129371643},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5153581500053406},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5118605494499207},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46525177359580994},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.4396919012069702},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.390541672706604},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3514993190765381}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8025393486022949},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7915583848953247},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7453123927116394},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7408262491226196},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7271828651428223},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.7167300581932068},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5952044129371643},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5153581500053406},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5118605494499207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46525177359580994},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.4396919012069702},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.390541672706604},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3514993190765381},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3115/v1/p14-1077","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/p14-1077","pdf_url":"https://aclanthology.org/P14-1077.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.654.1722","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.654.1722","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://aclweb.org/anthology/P/P14/P14-1077.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/v1/p14-1077","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/p14-1077","pdf_url":"https://aclanthology.org/P14-1077.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2299981395","display_name":null,"funder_award_id":"A011101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4328479870","display_name":null,"funder_award_id":"61272344","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5249178904","display_name":null,"funder_award_id":"Grant No. 6","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5857969212","display_name":null,"funder_award_id":"61370055","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6301336913","display_name":null,"funder_award_id":"6137005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8325939707","display_name":null,"funder_award_id":"61202233","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2251421040.pdf","grobid_xml":"https://content.openalex.org/works/W2251421040.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W111534319","https://openalex.org/W174427690","https://openalex.org/W1493490255","https://openalex.org/W1502749598","https://openalex.org/W1604644367","https://openalex.org/W1852412531","https://openalex.org/W2042610832","https://openalex.org/W2107598941","https://openalex.org/W2127978399","https://openalex.org/W2128851024","https://openalex.org/W2132679783","https://openalex.org/W2145453687","https://openalex.org/W2146191280","https://openalex.org/W2149713870","https://openalex.org/W2150588363","https://openalex.org/W2153225416","https://openalex.org/W2165962657","https://openalex.org/W2167187514","https://openalex.org/W2251532992","https://openalex.org/W2401642934"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W3114793362"],"abstract_inverted_index":{"Most":[0],"existing":[1,113],"relation":[2,93],"extraction":[3,94],"models":[4,95],"make":[5],"predictions":[6,30],"for":[7],"each":[8],"entity":[9,33],"pair":[10],"locally":[11],"and":[12,70,84],"individually,":[13],"while":[14],"ignoring":[15],"implicit":[16,68],"global":[17,47],"clues":[18,48,60,98,109],"available":[19],"in":[20,81],"the":[21,67,91,102,108],"knowledge":[22,114],"base,":[23],"sometimes":[24],"leading":[25],"to":[26,49,61,101,118],"conflicts":[27],"among":[28,52],"local":[29,53],"from":[31,112],"different":[32],"pairs.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38,105],"propose":[39],"a":[40,74],"joint":[41],"inference":[42],"framework":[43,89],"that":[44,87,107],"utilizes":[45],"these":[46],"resolve":[50],"disagreements":[51],"predictions.":[54],"We":[55],"exploit":[56],"two":[57],"kinds":[58],"of":[59,73],"generate":[62],"constraints":[63],"which":[64],"can":[65],"capture":[66],"type":[69],"cardinality":[71],"requirements":[72],"relation.":[75],"Experimental":[76],"results":[77],"on":[78],"three":[79],"datasets,":[80],"both":[82],"English":[83],"Chinese,":[85],"show":[86],"our":[88],"outperforms":[90],"state-of-theart":[92],"when":[96],"such":[97],"are":[99],"applicable":[100],"datasets.":[103],"And,":[104],"find":[106],"learnt":[110],"automatically":[111],"bases":[115],"perform":[116],"comparably":[117],"those":[119],"refined":[120],"by":[121],"human.":[122]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}
