{"id":"https://openalex.org/W2462574031","doi":"https://doi.org/10.18653/v1/w16-2520","title":"Correlation-based Intrinsic Evaluation of Word Vector Representations","display_name":"Correlation-based Intrinsic Evaluation of Word Vector Representations","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2462574031","doi":"https://doi.org/10.18653/v1/w16-2520","mag":"2462574031"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w16-2520","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w16-2520","pdf_url":"https://aclanthology.org/W16-2520.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 1st Workshop on Evaluating Vector-Space\n          Representations for NLP","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/W16-2520.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062910836","display_name":"Yulia Tsvetkov","orcid":"https://orcid.org/0000-0002-4634-7128"},"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"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yulia Tsvetkov","raw_affiliation_strings":["Carnegie Mellon University  Google DeepMind","Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University  Google DeepMind","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014855689","display_name":"Manaal Faruqui","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"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manaal Faruqui","raw_affiliation_strings":["Carnegie Mellon University  Google DeepMind","Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University  Google DeepMind","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111222692","display_name":"Chris Dyer","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"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris Dyer","raw_affiliation_strings":["Carnegie Mellon University  Google DeepMind","Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University  Google DeepMind","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062910836"],"corresponding_institution_ids":["https://openalex.org/I1291425158","https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.03537223,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"111","last_page":"115"},"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":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/T12031","display_name":"Speech and dialogue systems","score":0.9939000010490417,"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/word","display_name":"Word (group theory)","score":0.7463349103927612},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6766330003738403},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6292169690132141},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6221605539321899},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6146416068077087},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5768254995346069},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5658765435218811},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.526111900806427},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5150108337402344},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.4995427131652832},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2724260687828064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18212687969207764}],"concepts":[{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7463349103927612},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6766330003738403},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6292169690132141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6221605539321899},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6146416068077087},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5768254995346069},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5658765435218811},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.526111900806427},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5150108337402344},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.4995427131652832},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2724260687828064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18212687969207764},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/w16-2520","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w16-2520","pdf_url":"https://aclanthology.org/W16-2520.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 1st Workshop on Evaluating Vector-Space\n          Representations for NLP","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1606.06710","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1606.06710","pdf_url":"https://arxiv.org/pdf/1606.06710","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":"","raw_type":"text"},{"id":"mag:2462574031","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1606.06710.pdf","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1606.06710","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1606.06710","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":"article"}],"best_oa_location":{"id":"doi:10.18653/v1/w16-2520","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w16-2520","pdf_url":"https://aclanthology.org/W16-2520.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 1st Workshop on Evaluating Vector-Space\n          Representations for NLP","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6899999976158142,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G357853969","display_name":"RI: Small: Modeling Lexical Borrowing to Bridge the \"Linguistic Divide\" in Natural Language Processing","funder_award_id":"1526745","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2462574031.pdf","grobid_xml":"https://content.openalex.org/works/W2462574031.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1593045043","https://openalex.org/W1632114991","https://openalex.org/W2038721957","https://openalex.org/W2065157922","https://openalex.org/W2067438047","https://openalex.org/W2100235303","https://openalex.org/W2126530744","https://openalex.org/W2137735870","https://openalex.org/W2157807817","https://openalex.org/W2158139315","https://openalex.org/W2250192802","https://openalex.org/W2250539671","https://openalex.org/W2251066368","https://openalex.org/W2251253014","https://openalex.org/W2251830157","https://openalex.org/W2251874715","https://openalex.org/W2296283641","https://openalex.org/W2949952998","https://openalex.org/W2950577311","https://openalex.org/W2951683451","https://openalex.org/W2964153729"],"related_works":["https://openalex.org/W2963482440","https://openalex.org/W2950577311","https://openalex.org/W2772528510","https://openalex.org/W2346530426","https://openalex.org/W3091814528","https://openalex.org/W2945405384","https://openalex.org/W2751634978","https://openalex.org/W2891378076","https://openalex.org/W2805334656","https://openalex.org/W3181603082","https://openalex.org/W2808355064","https://openalex.org/W2883309503","https://openalex.org/W2536575021","https://openalex.org/W2903988268","https://openalex.org/W2251534017","https://openalex.org/W2574738034","https://openalex.org/W3137093496","https://openalex.org/W3207073167","https://openalex.org/W1523867809","https://openalex.org/W2987855688"],"abstract_inverted_index":{"We":[0,22,40],"introduce":[1],"QVEC-CCA-an":[2],"intrinsic":[3,61],"evaluation":[4,46,62],"metric":[5],"for":[6,31],"word":[7,64,70],"vector":[8],"representations":[9],"based":[10,68],"on":[11,69],"correlations":[12,52],"of":[13,34,63],"learned":[14],"vectors":[15,65],"with":[16,53],"features":[17],"extracted":[18],"from":[19],"linguistic":[20],"resources.":[21],"show":[23,42],"that":[24,43,66],"QVEC-CCA":[25],"scores":[26],"are":[27,67],"an":[28],"effective":[29],"proxy":[30],"a":[32],"range":[33],"extrinsic":[35],"semantic":[36],"and":[37,49],"syntactic":[38],"tasks.":[39],"also":[41],"the":[44],"proposed":[45],"obtains":[47],"higher":[48],"more":[50],"consistent":[51],"downstream":[54],"tasks,":[55],"compared":[56],"to":[57,60],"existing":[58],"approaches":[59],"similarity.":[71]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}
