{"id":"https://openalex.org/W3041387857","doi":"https://doi.org/10.24963/ijcai.2020/686","title":"Incorporating Extra Knowledge to Enhance Word Embedding","display_name":"Incorporating Extra Knowledge to Enhance Word Embedding","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3041387857","doi":"https://doi.org/10.24963/ijcai.2020/686","mag":"3041387857"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/686","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/686","pdf_url":"https://www.ijcai.org/proceedings/2020/0686.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0686.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101625623","display_name":"Arpita Roy","orcid":"https://orcid.org/0009-0007-7472-174X"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arpita Roy","raw_affiliation_strings":["University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048111120","display_name":"Shimei Pan","orcid":"https://orcid.org/0000-0002-5989-8543"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shimei Pan","raw_affiliation_strings":["University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101625623"],"corresponding_institution_ids":["https://openalex.org/I79272384"],"apc_list":null,"apc_paid":null,"fwci":1.6321,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.87331484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4929","last_page":"4935"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/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/T13629","display_name":"Text Readability and Simplification","score":0.9911999702453613,"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":0.8377127051353455},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.776803731918335},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7034022212028503},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.691689133644104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6898843050003052},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6431090831756592},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5706115365028381},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5395618677139282},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5252820253372192},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5130071043968201},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.45750004053115845},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.18758830428123474}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8377127051353455},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.776803731918335},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7034022212028503},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.691689133644104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6898843050003052},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6431090831756592},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5706115365028381},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5395618677139282},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5252820253372192},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5130071043968201},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.45750004053115845},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.18758830428123474},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/686","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/686","pdf_url":"https://www.ijcai.org/proceedings/2020/0686.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/686","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/686","pdf_url":"https://www.ijcai.org/proceedings/2020/0686.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3041387857.pdf","grobid_xml":"https://content.openalex.org/works/W3041387857.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W68293321","https://openalex.org/W1503259811","https://openalex.org/W1614298861","https://openalex.org/W1986321089","https://openalex.org/W2158028897","https://openalex.org/W2159583324","https://openalex.org/W2250683455","https://openalex.org/W2250879510","https://openalex.org/W2251012068","https://openalex.org/W2252143362","https://openalex.org/W2294979170","https://openalex.org/W2509435444","https://openalex.org/W2567035470","https://openalex.org/W2735288945","https://openalex.org/W2739945392","https://openalex.org/W2741247830","https://openalex.org/W2758506174","https://openalex.org/W2788694125","https://openalex.org/W2798962680","https://openalex.org/W2835115886","https://openalex.org/W2896457183","https://openalex.org/W2945014040","https://openalex.org/W2950018712","https://openalex.org/W2953356739","https://openalex.org/W2962739339","https://openalex.org/W2970986510"],"related_works":["https://openalex.org/W947140380","https://openalex.org/W4286432911","https://openalex.org/W4230884544","https://openalex.org/W4245453790","https://openalex.org/W3194985222","https://openalex.org/W3216571906","https://openalex.org/W4214830338","https://openalex.org/W2518587255","https://openalex.org/W4287599800","https://openalex.org/W4385432812"],"abstract_inverted_index":{"Word":[0],"embedding,":[1],"a":[2,18,30,74,123],"process":[3],"to":[4,65,105],"automatically":[5],"learn":[6],"the":[7,26,33,39,47,67,98,113],"mathematical":[8],"representations":[9],"of":[10,20,29,45,115],"words":[11,24,72],"from":[12],"unlabeled":[13],"text":[14],"corpora,":[15],"has":[16],"gained":[17],"lot":[19],"attention":[21],"recently.":[22],"Since":[23,58],"are":[25,62],"basic":[27],"units":[28],"natural":[31],"language,":[32],"more":[34],"precisely":[35],"we":[36,49,96],"can":[37,50],"represent":[38],"morphological,":[40],"syntactic":[41],"and":[42],"semantic":[43,68],"properties":[44],"words,":[46],"better":[48],"support":[51],"downstream":[52],"Natural":[53],"Language":[54],"Processing":[55],"(NLP)":[56],"tasks.":[57],"traditional":[59],"word":[60,107],"embeddings":[61],"mainly":[63],"designed":[64],"capture":[66],"relatedness":[69],"between":[70],"co-occurred":[71],"in":[73,82,101],"predefined":[75],"context,":[76],"it":[77],"may":[78],"not":[79],"be":[80],"effective":[81],"encoding":[83],"other":[84],"information":[85],"that":[86],"is":[87],"important":[88],"for":[89],"different":[90],"NLP":[91],"applications.":[92],"In":[93],"this":[94],"survey,":[95],"summarize":[97],"recent":[99],"advances":[100],"incorporating":[102],"extra":[103],"knowledge":[104],"enhance":[106],"embedding.":[108],"We":[109],"will":[110],"also":[111],"identify":[112],"limitations":[114],"existing":[116],"work":[117],"as":[118,120],"well":[119],"point":[121],"out":[122],"few":[124],"promising":[125],"future":[126],"directions.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}