{"id":"https://openalex.org/W2922365492","doi":"https://doi.org/10.1109/icosc.2019.8665508","title":"HWE: Word Embedding with Heterogeneous Features","display_name":"HWE: Word Embedding with Heterogeneous Features","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2922365492","doi":"https://doi.org/10.1109/icosc.2019.8665508","mag":"2922365492"},"language":"en","primary_location":{"id":"doi:10.1109/icosc.2019.8665508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icosc.2019.8665508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 13th International Conference on Semantic Computing (ICSC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087731094","display_name":"Jhih-Sheng Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Jhih-Sheng Fan","raw_affiliation_strings":["Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan"],"affiliations":[{"raw_affiliation_string":"Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113960258","display_name":"Mu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Mu Yang","raw_affiliation_strings":["Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan"],"affiliations":[{"raw_affiliation_string":"Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049915362","display_name":"Peng-Hsuan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Peng-Hsuan Li","raw_affiliation_strings":["Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan"],"affiliations":[{"raw_affiliation_string":"Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079299931","display_name":"Wei-Yun Ma","orcid":"https://orcid.org/0000-0002-4094-7464"},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wei-Yun Ma","raw_affiliation_strings":["Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan"],"affiliations":[{"raw_affiliation_string":"Academia Sinica, Institute of Information Science, Nankang, Taipei 115, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5087731094"],"corresponding_institution_ids":["https://openalex.org/I4210098366"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.02113696,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"39","last_page":"46"},"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":0.9995999932289124,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9941999912261963,"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":0.8403420448303223},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.7913142442703247},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7147213816642761},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7143577933311462},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6223154067993164},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.62212735414505},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.5873847603797913},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5827827453613281},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5763675570487976},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.41684022545814514},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.11880013346672058},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0879645049571991}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8403420448303223},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.7913142442703247},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7147213816642761},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7143577933311462},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6223154067993164},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.62212735414505},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.5873847603797913},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5827827453613281},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5763675570487976},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41684022545814514},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11880013346672058},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0879645049571991},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icosc.2019.8665508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icosc.2019.8665508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 13th International Conference on Semantic Computing (ICSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1499253590","https://openalex.org/W1503259811","https://openalex.org/W1854884267","https://openalex.org/W1880262756","https://openalex.org/W1970689298","https://openalex.org/W2026487812","https://openalex.org/W2067438047","https://openalex.org/W2080100102","https://openalex.org/W2081580037","https://openalex.org/W2103318667","https://openalex.org/W2112184938","https://openalex.org/W2120861206","https://openalex.org/W2123442489","https://openalex.org/W2128634885","https://openalex.org/W2130942839","https://openalex.org/W2132631284","https://openalex.org/W2133280805","https://openalex.org/W2142625445","https://openalex.org/W2153579005","https://openalex.org/W2158139315","https://openalex.org/W2158899491","https://openalex.org/W2160660844","https://openalex.org/W2170682101","https://openalex.org/W2176085882","https://openalex.org/W2238728730","https://openalex.org/W2250539671","https://openalex.org/W2250683455","https://openalex.org/W2250861254","https://openalex.org/W2250930514","https://openalex.org/W2251012068","https://openalex.org/W2251682575","https://openalex.org/W2251771443","https://openalex.org/W2251939518","https://openalex.org/W2306538696","https://openalex.org/W2466573295","https://openalex.org/W2516087440","https://openalex.org/W2950797609","https://openalex.org/W2951943225","https://openalex.org/W2952230511","https://openalex.org/W2963620259","https://openalex.org/W2964160783","https://openalex.org/W2998704965","https://openalex.org/W4231510805","https://openalex.org/W4285719527","https://openalex.org/W4294170691","https://openalex.org/W6639619044","https://openalex.org/W6678800971","https://openalex.org/W6679436768","https://openalex.org/W6679840752","https://openalex.org/W6679915538","https://openalex.org/W6680532216","https://openalex.org/W6682691769","https://openalex.org/W6683557909","https://openalex.org/W6683738474","https://openalex.org/W6683895063","https://openalex.org/W6687809933","https://openalex.org/W6689951360","https://openalex.org/W6691459498","https://openalex.org/W6691746754"],"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":{"Distributed":[0],"word":[1,15,53,94,122],"representation":[2,99],"is":[3],"widely":[4],"used":[5],"in":[6,21,55],"Natural":[7],"Language":[8],"Processing.":[9],"However,":[10],"traditional":[11],"approaches,":[12],"which":[13,103],"learn":[14],"representations":[16],"from":[17],"the":[18,76,84,89,112,120],"co-occurrence":[19],"information":[20],"large":[22],"corpora,":[23],"might":[24],"not":[25],"capture":[26],"fine-grained":[27],"syntactic":[28],"and":[29,39,69,78,88],"semantic":[30],"information.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,110],"propose":[36],"a":[37],"general":[38],"flexible":[40],"framework":[41],"to":[42],"incorporate":[43],"heterogeneous":[44],"features":[45],"(e.g.,":[46],"word-sense,":[47],"part-of-speech,":[48],"topic)":[49],"for":[50],"learning":[51],"feature-specific":[52],"embeddings":[54],"an":[56],"explicit":[57],"fashion,":[58],"namely":[59],"Heterogeneous":[60],"Word":[61],"Embedding":[62],"(HWE).":[63],"Experimental":[64],"results":[65],"on":[66],"both":[67],"intrinsic":[68],"extrinsic":[70],"tasks":[71],"show":[72],"that":[73],"HWE":[74,87],"outperforms":[75],"baseline":[77],"various":[79],"state-of-the-art":[80],"models.":[81],"Moreover,":[82],"through":[83],"concatenation":[85],"over":[86],"corresponding":[90],"feature":[91],"embeddings,":[92],"each":[93],"would":[95],"have":[96],"different":[97,101],"contextual":[98],"under":[100],"contexts,":[102],"achieves":[104],"even":[105],"more":[106],"significant":[107],"improvement.":[108],"Finally,":[109],"illustrate":[111],"insight":[113],"of":[114,119],"our":[115],"model":[116],"via":[117],"visualization":[118],"learned":[121],"embeddings.":[123]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}