{"id":"https://openalex.org/W2250481150","doi":"https://doi.org/10.18653/v1/d13-1041","title":"Efficient Collective Entity Linking with Stacking","display_name":"Efficient Collective Entity Linking with Stacking","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2250481150","doi":"https://doi.org/10.18653/v1/d13-1041","mag":"2250481150"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d13-1041","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d13-1041","pdf_url":"https://aclanthology.org/D13-1041.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 2013 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/D13-1041.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008819631","display_name":"Zhengyan He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhengyan He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101635405","display_name":"Shujie Liu","orcid":"https://orcid.org/0009-0008-2599-6752"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shujie Liu","raw_affiliation_strings":["(Microsoft)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"(Microsoft)","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100688422","display_name":"Yang Song","orcid":"https://orcid.org/0000-0001-8252-9626"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yang Song","raw_affiliation_strings":["(Microsoft)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"(Microsoft)","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100399466","display_name":"Mu Li","orcid":"https://orcid.org/0000-0002-7327-3304"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mu Li","raw_affiliation_strings":["(Microsoft)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"(Microsoft)","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701572","display_name":"Ming Zhou","orcid":"https://orcid.org/0000-0002-2551-2964"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025565222","display_name":"Houfeng Wang","orcid":"https://orcid.org/0000-0001-7130-1589"},"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":"Houfeng Wang","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"426","last_page":"435"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9986000061035156,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9979000091552734,"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":0.8192125558853149},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.661894679069519},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6501922607421875},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.6378431916236877},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5984442234039307},{"id":"https://openalex.org/keywords/entity-linking","display_name":"Entity linking","score":0.588126540184021},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5822727680206299},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5566655397415161},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5509136319160461},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5116243958473206},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4937983453273773},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.4622459411621094},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.42263007164001465},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.4149768650531769},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4148099720478058},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3983803987503052},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08895048499107361}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8192125558853149},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.661894679069519},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6501922607421875},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.6378431916236877},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5984442234039307},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.588126540184021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5822727680206299},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5566655397415161},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5509136319160461},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5116243958473206},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4937983453273773},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.4622459411621094},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.42263007164001465},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.4149768650531769},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4148099720478058},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3983803987503052},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08895048499107361},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/d13-1041","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d13-1041","pdf_url":"https://aclanthology.org/D13-1041.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 2013 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.593.6274","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.593.6274","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/D/D13/D13-1041.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/d13-1041","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d13-1041","pdf_url":"https://aclanthology.org/D13-1041.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 2013 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4243412236","display_name":null,"funder_award_id":"863 Program","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"},{"id":"https://openalex.org/F4320327557","display_name":"National Office for Philosophy and Social Sciences","ror":"https://ror.org/04m0ms912"},{"id":"https://openalex.org/F4320335773","display_name":"National High-tech Research and Development Program","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2250481150.pdf","grobid_xml":"https://content.openalex.org/works/W2250481150.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W11298561","https://openalex.org/W62652416","https://openalex.org/W86887328","https://openalex.org/W1534979469","https://openalex.org/W1548663377","https://openalex.org/W1574911124","https://openalex.org/W2002371870","https://openalex.org/W2047221353","https://openalex.org/W2067292826","https://openalex.org/W2085337304","https://openalex.org/W2100341149","https://openalex.org/W2115352105","https://openalex.org/W2129921015","https://openalex.org/W2136646623","https://openalex.org/W2137807925","https://openalex.org/W2151048449","https://openalex.org/W2159406587","https://openalex.org/W2167774378","https://openalex.org/W2188437695"],"related_works":["https://openalex.org/W1541691357","https://openalex.org/W2090135255","https://openalex.org/W2168409722","https://openalex.org/W4392237968","https://openalex.org/W2026505290","https://openalex.org/W2782437235","https://openalex.org/W1993715838","https://openalex.org/W2359088421","https://openalex.org/W2515501281","https://openalex.org/W2181629536"],"abstract_inverted_index":{"Entity":[0],"disambiguation":[1,18,36],"works":[2],"by":[3],"linking":[4],"ambiguous":[5],"mentions":[6],"in":[7,14,82],"text":[8],"to":[9,50,55,91,103],"their":[10],"corresponding":[11],"real-world":[12],"entities":[13],"knowledge":[15],"base.Recent":[16],"collective":[17,35],"methods":[19],"enforce":[20],"coherence":[21,74],"among":[22],"contextual":[23],"decisions":[24],"at":[25],"the":[26,83,93],"cost":[27],"of":[28,60,65],"non-trivial":[29],"inference":[30],"processes.We":[31],"propose":[32],"a":[33,43,116],"fast":[34,100],"approach":[37],"based":[38],"on":[39,111],"stacking.First,":[40],"we":[41],"train":[42],"local":[44],"predictor":[45,77],"g":[46,78],"0":[47],"with":[48],"learning":[49,115],"rank":[51],"as":[52],"base":[53],"learner,":[54],"generate":[56],"initial":[57],"ranking":[58],"list":[59],"candidates.Second,":[61],"top":[62],"k":[63],"candidates":[64],"related":[66],"instances":[67],"are":[68],"searched":[69],"for":[70],"constructing":[71],"expressive":[72],"global":[73,76],"features.A":[75],"1":[79],"is":[80,89,99,128],"trained":[81],"augmented":[84],"feature":[85],"space":[86],"and":[87,101,124],"stacking":[88],"employed":[90],"tackle":[92],"train/test":[94],"mismatch":[95],"problem.The":[96],"proposed":[97],"method":[98],"easy":[102],"implement.Experiments":[104],"show":[105],"its":[106],"effectiveness":[107],"over":[108],"various":[109],"algorithms":[110],"several":[112],"public":[113],"datasets.By":[114],"rich":[117],"semantic":[118],"relatedness":[119],"measure":[120],"between":[121],"entity":[122],"categories":[123],"context":[125],"document,":[126],"performance":[127],"further":[129],"improved.":[130]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
