{"id":"https://openalex.org/W1792926363","doi":"https://doi.org/10.18653/v1/d13-1136","title":"Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction","display_name":"Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W1792926363","doi":"https://doi.org/10.18653/v1/d13-1136","mag":"1792926363"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d13-1136","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d13-1136","pdf_url":"https://aclanthology.org/D13-1136.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":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/D13-1136.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076635608","display_name":"Jason Weston","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason Weston","raw_affiliation_strings":["GOOGLE INC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GOOGLE INC","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052390500","display_name":"Antoine Bordes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antoine Bordes","raw_affiliation_strings":["Laboratoire d'Excellence Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratoire d'Excellence Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033187873","display_name":"Oksana Yakhnenko","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oksana Yakhnenko","raw_affiliation_strings":["GOOGLE INC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GOOGLE INC","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084360449","display_name":"Nicolas Usunier","orcid":"https://orcid.org/0000-0002-9324-1457"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolas Usunier","raw_affiliation_strings":["Laboratoire d'Excellence Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratoire d'Excellence Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":44,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1366","last_page":"1371"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9997000098228455,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9959999918937683,"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/relationship-extraction","display_name":"Relationship extraction","score":0.7815701961517334},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7311896085739136},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6908290386199951},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6389378309249878},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5485660433769226},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4545707702636719},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.3938136398792267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3817495107650757},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.16744452714920044},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.07630118727684021}],"concepts":[{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7815701961517334},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7311896085739136},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6908290386199951},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6389378309249878},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5485660433769226},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4545707702636719},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3938136398792267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3817495107650757},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.16744452714920044},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.07630118727684021},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.18653/v1/d13-1136","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d13-1136","pdf_url":"https://aclanthology.org/D13-1136.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:arXiv.org:1307.7973","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1307.7973","pdf_url":"https://arxiv.org/pdf/1307.7973","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"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.593.1577","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.593.1577","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/D13-1136.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.745.7851","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.745.7851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1307.7973.pdf","raw_type":"text"},{"id":"pmh:oai:HAL:hal-00880455v1","is_oa":true,"landing_page_url":"https://hal.science/hal-00880455","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Conference on Empirical Methods in Natural Language Processing, Oct 2013, Seattle, United States. pp.1366-1371","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.1307.7973","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1307.7973","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/d13-1136","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d13-1136","pdf_url":"https://aclanthology.org/D13-1136.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":[{"display_name":"Quality Education","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G4009746975","display_name":null,"funder_award_id":"ANR-11-IDEX-0004-02","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G4866930200","display_name":null,"funder_award_id":"ANR-11-IDEX","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G5003875507","display_name":null,"funder_award_id":"-11-IDEX-0004-02","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G5615659713","display_name":null,"funder_award_id":"IDEX-0004-02","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G6182026652","display_name":null,"funder_award_id":"11-IDEX-0004","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G6246589203","display_name":null,"funder_award_id":"11-IDEX-0004-02","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G6654833908","display_name":"Sorbonne Universit\u00e9s \u00e0 Paris pour l'Enseignement et la Recherche","funder_award_id":"ANR-11-IDEX-0004","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"},{"id":"https://openalex.org/F4320338463","display_name":"CHIST-ERA","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1792926363.pdf"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W174427690","https://openalex.org/W205829674","https://openalex.org/W1493490255","https://openalex.org/W1512387364","https://openalex.org/W1596986901","https://openalex.org/W1604644367","https://openalex.org/W1852412531","https://openalex.org/W1923162067","https://openalex.org/W1950142954","https://openalex.org/W1954715867","https://openalex.org/W2096765155","https://openalex.org/W2102765684","https://openalex.org/W2107598941","https://openalex.org/W2112999531","https://openalex.org/W2115632234","https://openalex.org/W2116716943","https://openalex.org/W2127795553","https://openalex.org/W2132679783","https://openalex.org/W2146304342","https://openalex.org/W2148007012","https://openalex.org/W2161494021","https://openalex.org/W2247119764"],"related_works":["https://openalex.org/W2609844752","https://openalex.org/W3107474891","https://openalex.org/W2528512298","https://openalex.org/W2368651715","https://openalex.org/W2787051473","https://openalex.org/W3101091249","https://openalex.org/W4205612161","https://openalex.org/W4301098747","https://openalex.org/W3203458396","https://openalex.org/W4301790404"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,48,76],"novel":[4],"approach":[5,65],"for":[6],"relation":[7],"extraction":[8],"from":[9,19,23,47],"free":[10],"text":[11,21,94],"which":[12],"is":[13,28,66],"trained":[14],"to":[15,68,86],"jointly":[16],"use":[17,70],"information":[18,73],"the":[20,71],"and":[22,42,45],"existing":[24,89],"knowledge.":[25],"Our":[26],"model":[27],"based":[29],"on":[30,54,93],"two":[31],"scoring":[32],"functions":[33],"that":[34,63,91],"operate":[35],"by":[36,75],"learning":[37],"low-dimensional":[38],"embeddings":[39],"of":[40,43,79],"words":[41],"entities":[44],"relationships":[46],"knowledge":[49],"base.":[50],"We":[51],"empirically":[52],"show":[53],"New":[55],"York":[56],"Times":[57],"articles":[58],"aligned":[59],"with":[60],"Freebase":[61,80],"relations":[62],"our":[64],"able":[67],"efficiently":[69],"extra":[72],"provided":[74],"large":[77],"subset":[78],"data":[81],"(4M":[82],"entities,":[83],"23k":[84],"relationships)":[85],"improve":[87],"over":[88],"methods":[90],"rely":[92],"features":[95],"alone.":[96]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}