{"id":"https://openalex.org/W2963955300","doi":"https://doi.org/10.18653/v1/w18-3003","title":"Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons","display_name":"Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2963955300","doi":"https://doi.org/10.18653/v1/w18-3003","mag":"2963955300"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w18-3003","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-3003","pdf_url":"https://www.aclweb.org/anthology/W18-3003.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 Third Workshop on Representation Learning for NLP","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W18-3003.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079972849","display_name":"Hwiyeol Jo","orcid":null},"institutions":[{"id":"https://openalex.org/I2818286","display_name":"LG (United States)","ror":"https://ror.org/02b948n83","country_code":"US","type":"company","lineage":["https://openalex.org/I2818286","https://openalex.org/I4210131320"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hwiyeol Jo","raw_affiliation_strings":["AI Lab, LG Electronics"],"affiliations":[{"raw_affiliation_string":"AI Lab, LG Electronics","institution_ids":["https://openalex.org/I2818286"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021052817","display_name":"Stanley Jungkyu Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I2818286","display_name":"LG (United States)","ror":"https://ror.org/02b948n83","country_code":"US","type":"company","lineage":["https://openalex.org/I2818286","https://openalex.org/I4210131320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stanley Jungkyu Choi","raw_affiliation_strings":["AI Lab, LG Electronics"],"affiliations":[{"raw_affiliation_string":"AI Lab, LG Electronics","institution_ids":["https://openalex.org/I2818286"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079972849"],"corresponding_institution_ids":["https://openalex.org/I2818286"],"apc_list":null,"apc_paid":null,"fwci":0.8144,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.80766311,"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":"24","last_page":"29"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10028","display_name":"Topic Modeling","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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9961000084877014,"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/word2vec","display_name":"Word2vec","score":0.8068534135818481},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7569031715393066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7250149846076965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7088792324066162},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.6909433007240295},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6505528688430786},{"id":"https://openalex.org/keywords/vector-space","display_name":"Vector space","score":0.5067842602729797},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.5026099681854248},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.485376238822937},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.46035903692245483},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4379209280014038},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3641422688961029},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.220909982919693},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.08093038201332092}],"concepts":[{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.8068534135818481},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7569031715393066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7250149846076965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7088792324066162},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.6909433007240295},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6505528688430786},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.5067842602729797},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.5026099681854248},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.485376238822937},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.46035903692245483},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4379209280014038},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3641422688961029},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.220909982919693},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.08093038201332092},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w18-3003","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-3003","pdf_url":"https://www.aclweb.org/anthology/W18-3003.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 Third Workshop on Representation Learning for NLP","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w18-3003","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-3003","pdf_url":"https://www.aclweb.org/anthology/W18-3003.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 Third Workshop on Representation Learning for NLP","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963955300.pdf","grobid_xml":"https://content.openalex.org/works/W2963955300.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1503259811","https://openalex.org/W1614298861","https://openalex.org/W1762161369","https://openalex.org/W1854884267","https://openalex.org/W2053921957","https://openalex.org/W2067438047","https://openalex.org/W2072128103","https://openalex.org/W2080100102","https://openalex.org/W2081580037","https://openalex.org/W2101234009","https://openalex.org/W2112184938","https://openalex.org/W2115792525","https://openalex.org/W2153579005","https://openalex.org/W2158139315","https://openalex.org/W2187089797","https://openalex.org/W2250539671","https://openalex.org/W2251044566","https://openalex.org/W2251507550","https://openalex.org/W2296653400","https://openalex.org/W2493916176","https://openalex.org/W2561529111","https://openalex.org/W2950577311","https://openalex.org/W2964109726","https://openalex.org/W2964207259","https://openalex.org/W2964232431","https://openalex.org/W4213168938","https://openalex.org/W4231109964","https://openalex.org/W4294170691","https://openalex.org/W4300080862","https://openalex.org/W4386506836"],"related_works":["https://openalex.org/W2905749112","https://openalex.org/W3107848922","https://openalex.org/W1997182898","https://openalex.org/W2346530426","https://openalex.org/W3101154008","https://openalex.org/W3099354896","https://openalex.org/W2890749918","https://openalex.org/W4287599800","https://openalex.org/W2772765860","https://openalex.org/W4312264180"],"abstract_inverted_index":{"We":[0,109],"propose":[1],"post-processing":[2],"method":[3,23,99,116],"for":[4],"enriching":[5],"not":[6],"only":[7],"word":[8,39,106,120,131],"representation":[9],"but":[10],"also":[11,110],"its":[12],"vector":[13,77,121],"space":[14,78],"using":[15,79],"semantic":[16,48,89],"lexicons,":[17],"which":[18,83],"we":[19,95],"call":[20],"extrofitting.":[21],"The":[22],"consists":[24],"of":[25,55,69,105],"3":[26],"steps":[27,66],"as":[28,125,127],"follows:":[29],"(i)":[30],"Expanding":[31],"1":[32],"or":[33],"more":[34],"dimension(s)":[35,87],"on":[36,103,114],"all":[37],"the":[38,61,70,76,85],"vectors,":[40],"filling":[41,58],"with":[42,88,93],"their":[43],"representative":[44,53],"value.":[45],"(ii)":[46],"Transferring":[47],"knowledge":[49],"by":[50],"averaging":[51],"each":[52],"values":[54],"synonyms":[56,71],"and":[57],"them":[59],"in":[60,117],"expanded":[62,86],"dimension(s).":[63],"These":[64],"two":[65],"make":[67],"representations":[68],"close":[72],"together.":[73],"(iii)":[74],"Projecting":[75],"Linear":[80],"Discriminant":[81],"Analysis,":[82],"eliminates":[84],"knowledge.":[90],"When":[91],"experimenting":[92],"GloVe,":[94],"find":[96],"that":[97],"our":[98,115],"outperforms":[100],"Faruqui's":[101],"retrofitting":[102],"some":[104],"similarity":[107],"task.":[108],"report":[111],"further":[112],"analysis":[113],"respect":[118],"to":[119],"dimensions,":[122],"vocabulary":[123],"size":[124],"well":[126],"other":[128],"well-known":[129],"pretrained":[130],"vectors":[132],"(e.g.,":[133],"Word2Vec,":[134],"Fasttext).":[135]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}