{"id":"https://openalex.org/W2467278090","doi":"https://doi.org/10.18653/v1/s16-1101","title":"GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS","display_name":"GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2467278090","doi":"https://doi.org/10.18653/v1/s16-1101","mag":"2467278090"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s16-1101","is_oa":true,"landing_page_url":"http://doi.org/10.18653/v1/s16-1101","pdf_url":"https://doi.org/10.18653/v1/s16-1101","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.18653/v1/s16-1101","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002602581","display_name":"Hanan Aldarmaki","orcid":"https://orcid.org/0000-0003-1706-1777"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hanan Aldarmaki","raw_affiliation_strings":["(George Washington University)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"(George Washington University)","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038581447","display_name":"Mona Diab","orcid":"https://orcid.org/0000-0002-7696-1436"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mona Diab","raw_affiliation_strings":["(George Washington University)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"(George Washington University)","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002602581"],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":1.7668,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.89166003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"663","last_page":"667"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9904000163078308,"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":0.8080039024353027},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7297031879425049},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7225592732429504},{"id":"https://openalex.org/keywords/factoring","display_name":"Factoring","score":0.6726398468017578},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.671176552772522},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6097159385681152},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.579327404499054},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5606231689453125},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4845522940158844},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.4751203656196594},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.44496017694473267},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.42302292585372925},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.1660219430923462},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13980841636657715}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8080039024353027},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7297031879425049},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7225592732429504},{"id":"https://openalex.org/C177225278","wikidata":"https://www.wikidata.org/wiki/Q192674","display_name":"Factoring","level":2,"score":0.6726398468017578},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.671176552772522},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6097159385681152},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.579327404499054},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5606231689453125},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4845522940158844},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.4751203656196594},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.44496017694473267},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.42302292585372925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.1660219430923462},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13980841636657715},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/s16-1101","is_oa":true,"landing_page_url":"http://doi.org/10.18653/v1/s16-1101","pdf_url":"https://doi.org/10.18653/v1/s16-1101","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/s16-1101","is_oa":true,"landing_page_url":"http://doi.org/10.18653/v1/s16-1101","pdf_url":"https://doi.org/10.18653/v1/s16-1101","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W11511616","https://openalex.org/W22168010","https://openalex.org/W2081580037","https://openalex.org/W2101409192","https://openalex.org/W2116772053","https://openalex.org/W2117130368","https://openalex.org/W2123442489","https://openalex.org/W2126725946","https://openalex.org/W2131744502","https://openalex.org/W2133280805","https://openalex.org/W2135875128","https://openalex.org/W2174932614","https://openalex.org/W2250385423","https://openalex.org/W2250539671","https://openalex.org/W2251033195","https://openalex.org/W2251805974","https://openalex.org/W2917452219","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2377710379","https://openalex.org/W2947911821","https://openalex.org/W2393473966","https://openalex.org/W2260256814","https://openalex.org/W2752041471","https://openalex.org/W78638240","https://openalex.org/W2252122760","https://openalex.org/W2105461184","https://openalex.org/W2135511601","https://openalex.org/W4387359329"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,34],"matrix":[3],"factorization":[4],"model":[5,17],"for":[6,10],"learning":[7],"cross-lingual":[8],"representations":[9,20],"sentences.":[11],"Using":[12],"sentence-aligned":[13],"corpora,":[14],"the":[15,23,52],"proposed":[16],"learns":[18],"distributed":[19],"by":[21],"factoring":[22],"given":[24],"data":[25],"into":[26,44],"language-dependent":[27],"factors":[28],"and":[29,47],"one":[30],"shared":[31],"factor.":[32],"As":[33],"result,":[35],"input":[36],"sentences":[37],"from":[38],"both":[39],"languages":[40],"can":[41],"be":[42],"mapped":[43],"fixed-length":[45],"vectors":[46],"then":[48],"compared":[49],"directly":[50],"using":[51],"cosine":[53],"similarity":[54],"measure,":[55],"which":[56],"achieves":[57],"0.8":[58],"Pearson":[59],"correlation":[60],"on":[61],"Spanish-English":[62],"semantic":[63],"textual":[64],"similarity.":[65]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-05-19T21:40:30.786675","created_date":"2025-10-10T00:00:00"}