{"id":"https://openalex.org/W2886622678","doi":"https://doi.org/10.18653/v1/w18-3011","title":"A Hybrid Learning Scheme for Chinese Word Embedding","display_name":"A Hybrid Learning Scheme for Chinese Word Embedding","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2886622678","doi":"https://doi.org/10.18653/v1/w18-3011","mag":"2886622678"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w18-3011","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-3011","pdf_url":"https://www.aclweb.org/anthology/W18-3011.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/W18-3011.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103061397","display_name":"Wenfan Chen","orcid":"https://orcid.org/0000-0002-2636-7914"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenfan Chen","raw_affiliation_strings":["School of Computer Science and Technology, Zhejiang University of Technology, Hang-zhou, P.R.China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Zhejiang University of Technology, Hang-zhou, P.R.China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019915591","display_name":"Weiguo Sheng","orcid":"https://orcid.org/0000-0001-9680-5126"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiguo Sheng","raw_affiliation_strings":["Department of Computer Science, Hangzhou Normal University, Hangzhou, P.R.China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Hangzhou Normal University, Hangzhou, P.R.China","institution_ids":["https://openalex.org/I163151501"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019915591"],"corresponding_institution_ids":["https://openalex.org/I163151501"],"apc_list":null,"apc_paid":null,"fwci":0.6769,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77638118,"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":"84","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9998000264167786,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9959999918937683,"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-embedding","display_name":"Word embedding","score":0.7935848236083984},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.7725361585617065},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7689287662506104},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7449827790260315},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7394203543663025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5734938979148865},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5144853591918945},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.40207821130752563},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3303355574607849},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1412326693534851}],"concepts":[{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.7935848236083984},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.7725361585617065},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7689287662506104},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7449827790260315},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394203543663025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5734938979148865},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5144853591918945},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40207821130752563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3303355574607849},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1412326693534851},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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:10.18653/v1/w18-3011","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-3011","pdf_url":"https://www.aclweb.org/anthology/W18-3011.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/w18-3011","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-3011","pdf_url":"https://www.aclweb.org/anthology/W18-3011.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":[{"display_name":"Quality Education","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G2802912691","display_name":null,"funder_award_id":"61573316","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5674688670","display_name":null,"funder_award_id":"6157331","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2886622678.pdf","grobid_xml":"https://content.openalex.org/works/W2886622678.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1544827683","https://openalex.org/W1614298861","https://openalex.org/W1787856957","https://openalex.org/W2105103432","https://openalex.org/W2130942839","https://openalex.org/W2131462252","https://openalex.org/W2133564696","https://openalex.org/W2142377809","https://openalex.org/W2153579005","https://openalex.org/W2250189634","https://openalex.org/W2251012068","https://openalex.org/W2251131401","https://openalex.org/W2394700483","https://openalex.org/W2417763662","https://openalex.org/W2463895987","https://openalex.org/W2468476969","https://openalex.org/W2493916176","https://openalex.org/W2557321750","https://openalex.org/W2566150155","https://openalex.org/W2759366113","https://openalex.org/W2788009253","https://openalex.org/W2949615363","https://openalex.org/W2950577311","https://openalex.org/W2964308564","https://openalex.org/W2998704965","https://openalex.org/W4285719527","https://openalex.org/W4294170691"],"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":{"To":[0],"improve":[1,77],"word":[2,40,54,64,81],"embedding,":[3],"subword":[4],"information":[5],"has":[6,59],"been":[7,60],"widely":[8],"employed":[9],"in":[10,83],"state-of-the-art":[11],"methods.":[12],"These":[13],"methods":[14],"can":[15,45,75],"be":[16],"classified":[17],"to":[18,62,91],"either":[19],"compositional":[20,35],"or":[21],"predictive":[22,37],"models.":[23],"In":[24],"this":[25],"paper,":[26],"we":[27],"propose":[28],"a":[29,43],"hybrid":[30],"learning":[31,53],"scheme,":[32],"which":[33],"integrates":[34],"and":[36,88],"model":[38],"for":[39],"embedding.":[41,55],"Such":[42],"scheme":[44,58,74],"take":[46],"advantage":[47],"of":[48,80,85,94],"both":[49],"models,":[50],"thus":[51],"effectively":[52],"The":[56],"proposed":[57,73],"applied":[61],"learn":[63],"representation":[65],"on":[66],"Chinese.":[67],"Our":[68],"results":[69],"show":[70],"that":[71],"the":[72,78,92],"significantly":[76],"performance":[79],"embedding":[82],"terms":[84],"analogical":[86],"reasoning":[87],"is":[89],"robust":[90],"size":[93],"training":[95],"data.":[96]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
