{"id":"https://openalex.org/W2984147501","doi":"https://doi.org/10.18653/v1/k19-1063","title":"Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking","display_name":"Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2984147501","doi":"https://doi.org/10.18653/v1/k19-1063","mag":"2984147501"},"language":"en","primary_location":{"id":"doi:10.18653/v1/k19-1063","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k19-1063","pdf_url":"https://www.aclweb.org/anthology/K19-1063.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 23rd Conference on Computational Natural Language Learning (CoNLL)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/K19-1063.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010181302","display_name":"Samuel Broscheit","orcid":null},"institutions":[{"id":"https://openalex.org/I177802217","display_name":"University of Mannheim","ror":"https://ror.org/031bsb921","country_code":"DE","type":"education","lineage":["https://openalex.org/I177802217"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Samuel Broscheit","raw_affiliation_strings":["Data and Web Science Group, University of Mannheim, Germany"],"affiliations":[{"raw_affiliation_string":"Data and Web Science Group, University of Mannheim, Germany","institution_ids":["https://openalex.org/I177802217"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5010181302"],"corresponding_institution_ids":["https://openalex.org/I177802217"],"apc_list":null,"apc_paid":null,"fwci":9.2484,"has_fulltext":true,"cited_by_count":93,"citation_normalized_percentile":{"value":0.98272731,"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":"677","last_page":"685"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9998999834060669,"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/T13629","display_name":"Text Readability and Simplification","score":0.9941999912261963,"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":0.8872973918914795},{"id":"https://openalex.org/keywords/entity-linking","display_name":"Entity linking","score":0.8404057025909424},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.7155678272247314},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7139055728912354},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6529403924942017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6080037355422974},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.599811851978302},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5966973304748535},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.5760804414749146},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5449404716491699},{"id":"https://openalex.org/keywords/named-entity","display_name":"Named entity","score":0.543851912021637},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5234850645065308},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.47590669989585876},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.4492073953151703},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.39550256729125977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8872973918914795},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.8404057025909424},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.7155678272247314},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7139055728912354},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6529403924942017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6080037355422974},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.599811851978302},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5966973304748535},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.5760804414749146},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5449404716491699},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.543851912021637},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5234850645065308},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.47590669989585876},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.4492073953151703},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39550256729125977},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/k19-1063","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k19-1063","pdf_url":"https://www.aclweb.org/anthology/K19-1063.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 23rd Conference on Computational Natural Language Learning (CoNLL)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2003.05473","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.05473","pdf_url":"https://arxiv.org/pdf/2003.05473","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/k19-1063","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k19-1063","pdf_url":"https://www.aclweb.org/anthology/K19-1063.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 23rd Conference on Computational Natural Language Learning (CoNLL)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6700000166893005,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1557924762","display_name":null,"funder_award_id":"TITAN X","funder_id":"https://openalex.org/F4320309480","funder_display_name":"Nvidia"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2984147501.pdf","grobid_xml":"https://content.openalex.org/works/W2984147501.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W11298561","https://openalex.org/W167875927","https://openalex.org/W1522301498","https://openalex.org/W2135451108","https://openalex.org/W2293004735","https://openalex.org/W2295227292","https://openalex.org/W2471349142","https://openalex.org/W2613223139","https://openalex.org/W2888236192","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2922551710","https://openalex.org/W2923014074","https://openalex.org/W2933138175","https://openalex.org/W2946417913","https://openalex.org/W2953356739","https://openalex.org/W2962739339","https://openalex.org/W2963026768","https://openalex.org/W2963159690","https://openalex.org/W2963310665","https://openalex.org/W2963323070","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963855739","https://openalex.org/W2964121744","https://openalex.org/W2970352191","https://openalex.org/W4381683870","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2186562580","https://openalex.org/W2155874911","https://openalex.org/W4255258373","https://openalex.org/W2032007337","https://openalex.org/W3000685722","https://openalex.org/W1884363728","https://openalex.org/W4253099099","https://openalex.org/W4386977977","https://openalex.org/W4200491110","https://openalex.org/W4313162113"],"abstract_inverted_index":{"A":[0],"typical":[1],"architecture":[2],"for":[3,38,204],"end-to-end":[4],"entity":[5,17,50,61,79,97,109,118,127,151,201],"linking":[6,80,110,128],"systems":[7],"consists":[8],"of":[9,77,159,193],"three":[10],"steps:":[11],"mention":[12,148],"detection,":[13],"candidate":[14],"generation":[15],"and":[16,135,150,176,178],"disambiguation.":[18],"In":[19],"this":[20,70,114],"study":[21],"we":[22,72,155,189],"investigate":[23,156],"the":[24,78,95,117,132,143,157,163,170,180,212],"following":[25],"questions:":[26],"(a)":[27],"Can":[28],"all":[29],"those":[30,194],"steps":[31],"be":[32],"learned":[33],"jointly":[34],"with":[35,207],"a":[36,90,205],"model":[37,115],"contextualized":[39],"text-representations,":[40],"i.e.":[41],"BERT":[42],"(Devlin":[43],"et":[44],"al.,":[45],"2019)?":[46],"(b)":[47],"How":[48],"much":[49],"knowledge":[51,62],"is":[52],"already":[53],"contained":[54],"in":[55,66,102,162,215],"pretrained":[56],"BERT?":[57],"(c)":[58],"Does":[59],"additional":[60,200],"improve":[63],"BERT's":[64],"performance":[65],"downstream":[67],"tasks?":[68],"To":[69,186],"end,":[71],"propose":[73],"an":[74,108],"extreme":[75],"simplification":[76],"setup":[81],"that":[82,112,124,130,137,146,191],"works":[83],"surprisingly":[84],"well:":[85],"simply":[86],"cast":[87],"it":[88,125,138],"as":[89,167,169],"per":[91],"token":[92],"classification":[93],"over":[94,120],"entire":[96],"vocabulary":[98],"(over":[99],"700K":[100],"classes":[101],"our":[103,187],"case).":[104],"We":[105],"show":[106],"on":[107],"benchmark":[111,165],"(i)":[113],"improves":[116,218],"representations":[119],"plain":[121],"BERT,":[122],"(ii)":[123],"outperforms":[126],"architectures":[129],"optimize":[131],"tasks":[133],"separately":[134],"(iii)":[136],"only":[139],"comes":[140],"second":[141],"to":[142],"current":[144],"state-of-the-art":[145],"does":[147],"detection":[149],"disambiguation":[152],"jointly.":[153],"Additionally,":[154],"usefulness":[158],"entity-aware":[160],"token-representations":[161],"text-understanding":[164],"GLUE,":[166,216],"well":[168],"question":[171],"answering":[172],"benchmarks":[173,195],"SQUAD":[174],"V2":[175],"SWAG":[177],"also":[179],"EN-DE":[181],"WMT14":[182],"machine":[183],"translation":[184],"benchmark.":[185],"surprise,":[188],"find":[190],"most":[192],"do":[196],"not":[197],"benefit":[198],"from":[199],"knowledge,":[202],"except":[203],"task":[206,214],"very":[208],"small":[209],"training":[210],"data,":[211],"RTE":[213],"which":[217],"by":[219],"2%.":[220]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
