{"id":"https://openalex.org/W4283736757","doi":"https://doi.org/10.1145/3534678.3539340","title":"Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection","display_name":"Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4283736757","doi":"https://doi.org/10.1145/3534678.3539340"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539340","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539340","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5029498141","display_name":"Han Liu","orcid":"https://orcid.org/0000-0001-6921-2050"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Liu","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401204","display_name":"Feng Zhang","orcid":"https://orcid.org/0000-0001-7178-0735"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403816","display_name":"Xiaotong Zhang","orcid":"https://orcid.org/0000-0002-5013-8476"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaotong Zhang","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102290620","display_name":"Siyang Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyang Zhao","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101202464","display_name":"Junjie Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Sun","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102728606","display_name":"Hong Yu","orcid":"https://orcid.org/0000-0003-4807-1812"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Yu","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006269113","display_name":"Xianchao Zhang","orcid":"https://orcid.org/0000-0002-0180-3740"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianchao Zhang","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7112,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.94488995,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1079","last_page":"1087"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9987000226974487,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9987000226974487,"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":0.9984999895095825,"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":0.9984999895095825,"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":0.820014238357544},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7930098176002502},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6697975397109985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6352030038833618},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5845785140991211},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5324156284332275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4987478256225586},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4366607666015625}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.820014238357544},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7930098176002502},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6697975397109985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6352030038833618},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5845785140991211},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5324156284332275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4987478256225586},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4366607666015625}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539340","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539340","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7200000286102295,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1357272394","display_name":null,"funder_award_id":"62106035, 61876028","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5441420046","display_name":null,"funder_award_id":"DUT20RC(3)040, DUT20RC(3)066","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1622600386","https://openalex.org/W1934455055","https://openalex.org/W2148034183","https://openalex.org/W2187089797","https://openalex.org/W2604763608","https://openalex.org/W2606593809","https://openalex.org/W2742657630","https://openalex.org/W2747623286","https://openalex.org/W2905471643","https://openalex.org/W2963777632","https://openalex.org/W3035531117","https://openalex.org/W3091806974","https://openalex.org/W3106213915","https://openalex.org/W3175613352","https://openalex.org/W3177342236"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2597655663"],"abstract_inverted_index":{"Multi-label":[0],"aspect":[1,12,49,80,133,149,163],"category":[2,50,81],"detection":[3],"allows":[4],"a":[5,71],"given":[6],"review":[7],"sentence":[8],"to":[9,17,100,160],"contain":[10],"multiple":[11],"categories,":[13],"which":[14,45,105,125,168],"is":[15,34,57,169],"shown":[16],"be":[18,88],"more":[19,102],"practical":[20],"in":[21,42,59,138,165],"sentiment":[22],"analysis":[23],"and":[24,36,61],"attracting":[25],"increasing":[26],"attention.":[27],"As":[28],"annotating":[29],"large":[30],"amounts":[31],"of":[32,85],"data":[33,38],"time-consuming":[35],"labor-intensive,":[37],"scarcity":[39],"occurs":[40],"frequently":[41],"real-world":[43],"scenarios,":[44],"motivates":[46],"multi-label":[47,78,157],"few-shot":[48,79],"detection.":[51,82],"However,":[52],"research":[53],"on":[54,176],"this":[55,67],"problem":[56],"still":[58],"infancy":[60],"few":[62],"methods":[63],"are":[64,135],"available.":[65],"In":[66,151],"paper,":[68],"we":[69],"propose":[70],"novel":[72],"label-enhanced":[73],"prototypical":[74],"network":[75],"(LPN)":[76],"for":[77],"The":[83],"highlights":[84],"LPN":[86,184],"can":[87,106,185],"summarized":[89],"as":[90,97],"follows.":[91],"First,":[92],"it":[93,120,153],"leverages":[94],"label":[95,134],"description":[96],"auxiliary":[98],"knowledge":[99],"learn":[101],"discriminative":[103],"prototypes,":[104],"retain":[107],"aspect-relevant":[108],"information":[109],"while":[110,141],"eliminating":[111],"the":[112,128,131,145,162,166],"harmful":[113],"effect":[114],"caused":[115],"by":[116],"irrelevant":[117],"aspects.":[118],"Second,":[119],"integrates":[121],"with":[122,130,147],"contrastive":[123],"learning,":[124],"encourages":[126],"that":[127,180],"sentences":[129,146],"same":[132],"pulled":[136],"together":[137],"embedding":[139],"space":[140],"simultaneously":[142],"pushing":[143],"apart":[144],"different":[148],"labels.":[150],"addition,":[152],"introduces":[154],"an":[155],"adaptive":[156],"inference":[158],"module":[159],"predict":[161],"count":[164],"sentence,":[167],"simple":[170],"yet":[171],"effective.":[172],"Extensive":[173],"experimental":[174],"results":[175],"three":[177],"datasets":[178],"demonstrate":[179],"our":[181],"proposed":[182],"model":[183],"consistently":[186],"achieve":[187],"state-of-the-art":[188],"performance.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
