{"id":"https://openalex.org/W2963691861","doi":"https://doi.org/10.18653/v1/p18-1148","title":"Improving Entity Linking by Modeling Latent Relations between Mentions","display_name":"Improving Entity Linking by Modeling Latent Relations between Mentions","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2963691861","doi":"https://doi.org/10.18653/v1/p18-1148","mag":"2963691861"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-1148","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1148","pdf_url":"https://www.aclweb.org/anthology/P18-1148.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P18-1148.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061504918","display_name":"Phong Ba Le","orcid":"https://orcid.org/0000-0002-5612-1800"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Phong Le","raw_affiliation_strings":["University of Edinburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086717154","display_name":"Ivan Titov","orcid":"https://orcid.org/0000-0002-2583-1893"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB","NL"],"is_corresponding":true,"raw_author_name":"Ivan Titov","raw_affiliation_strings":["University of Amsterdam","University of Edinburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]},{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086717154"],"corresponding_institution_ids":["https://openalex.org/I887064364","https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":11.4313,"has_fulltext":true,"cited_by_count":211,"citation_normalized_percentile":{"value":0.98730557,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1595","last_page":"1604"},"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/T11719","display_name":"Data Quality and Management","score":0.9932000041007996,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9929999709129333,"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/coreference","display_name":"Coreference","score":0.9050877094268799},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8562635183334351},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.7385762333869934},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6497077941894531},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6495773196220398},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6373482942581177},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6142866611480713},{"id":"https://openalex.org/keywords/entity-linking","display_name":"Entity linking","score":0.6130813360214233},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.5538027286529541},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5138931274414062},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.47170305252075195},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.4257071316242218},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.42217472195625305},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4151528477668762},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38036373257637024},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.35495978593826294},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2246251404285431},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.14292126893997192}],"concepts":[{"id":"https://openalex.org/C28076734","wikidata":"https://www.wikidata.org/wiki/Q63087","display_name":"Coreference","level":3,"score":0.9050877094268799},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8562635183334351},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.7385762333869934},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6497077941894531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6495773196220398},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6373482942581177},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6142866611480713},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.6130813360214233},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.5538027286529541},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5138931274414062},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.47170305252075195},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.4257071316242218},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.42217472195625305},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4151528477668762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38036373257637024},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35495978593826294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2246251404285431},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.14292126893997192},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/p18-1148","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1148","pdf_url":"https://www.aclweb.org/anthology/P18-1148.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/3478abca-e395-45cd-b088-7bae72ee2716","is_oa":true,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/improving-entity-linking-by-modeling-latent-relations-between-mentions(3478abca-e395-45cd-b088-7bae72ee2716).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Le, P & Titov, I 2018, Improving entity linking by modeling latent relations between mentions. in I Gurevych & Y Miyao (eds), ACL 2018 : The 56th Annual Meeting of the Association for Computational Linguistics : proceedings of the conference : July 15-20, 2018, Melbourne, Australia. vol. 1, Stroudsburg, PA, pp. 1595-1604, 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15/07/18. https://doi.org/10.18653/v1/p18-1148","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:pure.ed.ac.uk:publications/c4e33d6b-2bb9-4812-91fd-d6c870068ddf","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/c4e33d6b-2bb9-4812-91fd-d6c870068ddf","pdf_url":"http://hdl.handle.net/20.500.11820/c4e33d6b-2bb9-4812-91fd-d6c870068ddf","source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Le, P & Titov, I 2018, Improving Entity Linking by Modeling Latent Relations between Mentions. in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Victoria, Australia, 15/07/18. https://doi.org/10.18653/v1/P18-1148","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-1148","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1148","pdf_url":"https://www.aclweb.org/anthology/P18-1148.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5226226009","display_name":"Scaling Semantic Parsing to Unrestricted Domains","funder_award_id":"639.022.518","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320310598","display_name":"Amazon Web Services","ror":"https://ror.org/04mv4n011"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963691861.pdf","grobid_xml":"https://content.openalex.org/works/W2963691861.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W11298561","https://openalex.org/W822806878","https://openalex.org/W1522301498","https://openalex.org/W1548663377","https://openalex.org/W1560512119","https://openalex.org/W1614298861","https://openalex.org/W2100341149","https://openalex.org/W2120340025","https://openalex.org/W2132679783","https://openalex.org/W2133564696","https://openalex.org/W2151048449","https://openalex.org/W2157191138","https://openalex.org/W2160424986","https://openalex.org/W2177768736","https://openalex.org/W2250539671","https://openalex.org/W2250869925","https://openalex.org/W2251064706","https://openalex.org/W2251079237","https://openalex.org/W2251894552","https://openalex.org/W2293004735","https://openalex.org/W2479758238","https://openalex.org/W2539469848","https://openalex.org/W2594284271","https://openalex.org/W2612773933","https://openalex.org/W2613223139","https://openalex.org/W2950577311","https://openalex.org/W2951450498","https://openalex.org/W2963403868","https://openalex.org/W2963855739","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2402725029","https://openalex.org/W2339319059","https://openalex.org/W2359367771","https://openalex.org/W2964120642","https://openalex.org/W4287018705","https://openalex.org/W2580855974","https://openalex.org/W1541691357","https://openalex.org/W2090135255","https://openalex.org/W4377865006","https://openalex.org/W2168409722"],"abstract_inverted_index":{"Entity":[0,17],"linking":[1,18,35],"involves":[2],"aligning":[3],"textual":[4,24],"mentions":[5,25],"of":[6],"named":[7],"entities":[8],"to":[9,31,49,112],"their":[10],"corresponding":[11],"entries":[12],"in":[13,26,59,76,115],"a":[14,27],"knowledge":[15],"base.":[16],"systems":[19,46],"often":[20],"exploit":[21],"relations":[22,55,67],"between":[23],"document":[28],"(e.g.,":[29],"coreference)":[30],"decide":[32],"if":[33],"the":[34,66,73,84,89,107,116],"decisions":[36],"are":[37],"compatible.":[38],"Unlike":[39],"previous":[40],"approaches,":[41],"which":[42],"relied":[43],"on":[44,88],"supervised":[45],"or":[47],"heuristics":[48],"predict":[50],"these":[51],"relations,":[52],"we":[53],"treat":[54],"as":[56],"latent":[57],"variables":[58],"our":[60],"neural":[61],"entity-linking":[62,74],"model.":[63],"We":[64],"induce":[65],"without":[68],"any":[69],"supervision":[70],"while":[71],"optimizing":[72],"system":[75],"an":[77],"end-to-end":[78],"fashion.":[79],"Our":[80],"multirelational":[81],"model":[82],"achieves":[83],"best":[85],"reported":[86],"scores":[87],"standard":[90],"benchmark":[91],"(AIDA-CoNLL)":[92],"and":[93],"substantially":[94],"outperforms":[95],"its":[96],"relation-agnostic":[97],"version.":[98],"Its":[99],"training":[100,117],"also":[101],"converges":[102],"much":[103],"faster,":[104],"suggesting":[105],"that":[106],"injected":[108],"structural":[109],"bias":[110],"helps":[111],"explain":[113],"regularities":[114],"data.":[118]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":33},{"year":2022,"cited_by_count":44},{"year":2021,"cited_by_count":32},{"year":2020,"cited_by_count":38},{"year":2019,"cited_by_count":30},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
