{"id":"https://openalex.org/W4312941963","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892431","title":"ACEs: Unsupervised Multi-label Aspect Detection with Aspect-category Experts","display_name":"ACEs: Unsupervised Multi-label Aspect Detection with Aspect-category Experts","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312941963","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892431"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892431","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5058909157","display_name":"Lingyu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lingyu Yang","raw_affiliation_strings":["Shenzhen International Graduate School Tsinghua University,Shenzhen,China","Shenzhen International Graduate School Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101911275","display_name":"Hongjia Li","orcid":"https://orcid.org/0000-0003-1683-343X"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongjia Li","raw_affiliation_strings":["Shenzhen International Graduate School Tsinghua University,Shenzhen,China","Shenzhen International Graduate School Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100440394","display_name":"Lei Li","orcid":"https://orcid.org/0000-0003-1959-1683"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["Shenzhen International Graduate School Tsinghua University,Shenzhen,China","Shenzhen International Graduate School Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101456902","display_name":"Chun Yuan","orcid":"https://orcid.org/0000-0002-3590-6676"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun Yuan","raw_affiliation_strings":["Shenzhen International Graduate School Tsinghua University,Shenzhen,China","Shenzhen International Graduate School Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110158722","display_name":"Shu\u2013Tao Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu-Tao Xia","raw_affiliation_strings":["Shenzhen International Graduate School Tsinghua University,Shenzhen,China","Shenzhen International Graduate School Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058909157"],"corresponding_institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16672053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 2101 190","issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9991999864578247,"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":0.9991999864578247,"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":0.998199999332428,"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/computer-science","display_name":"Computer science","score":0.7962683439254761},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5256527662277222},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4909529983997345},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4735614061355591},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.43029674887657166},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.41840147972106934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7962683439254761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5256527662277222},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4909529983997345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4735614061355591},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.43029674887657166},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.41840147972106934},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892431","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2113786470","https://openalex.org/W2129604374","https://openalex.org/W2252012216","https://openalex.org/W2465978385","https://openalex.org/W2741252866","https://openalex.org/W2798836702","https://openalex.org/W2888507208","https://openalex.org/W2896457183","https://openalex.org/W2899771611","https://openalex.org/W2947380870","https://openalex.org/W2962808042","https://openalex.org/W2962843214","https://openalex.org/W2963254338","https://openalex.org/W2964288660","https://openalex.org/W2966327190","https://openalex.org/W2970476646","https://openalex.org/W2971014768","https://openalex.org/W2971015282","https://openalex.org/W2990216037","https://openalex.org/W3005680577","https://openalex.org/W3035410788","https://openalex.org/W3035611181","https://openalex.org/W3100474067","https://openalex.org/W3106109117","https://openalex.org/W3110687497","https://openalex.org/W3115295967","https://openalex.org/W3131870090","https://openalex.org/W3156636935","https://openalex.org/W3173706378","https://openalex.org/W3174770825","https://openalex.org/W6677267668","https://openalex.org/W6679108089","https://openalex.org/W6681968150","https://openalex.org/W6726652845","https://openalex.org/W6751779834","https://openalex.org/W6755207826","https://openalex.org/W6756040250","https://openalex.org/W6774314701","https://openalex.org/W6779597862","https://openalex.org/W6787655056","https://openalex.org/W6791055906"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Unsupervised":[0],"aspect":[1,8,38,116,165],"detection":[2,166],"(UAD)":[3],"aims":[4],"to":[5,65,69,93,100,107,133,141,148,162],"identify":[6],"the":[7,28,50,53,109,125,135,143,164,181],"categories":[9,117],"mentioned":[10],"in":[11,58,111,121],"product":[12],"reviews":[13],"automatically.":[14],"Existing":[15],"unsupervised":[16,87],"methods":[17,63],"mainly":[18],"focus":[19],"on":[20,171],"single-label":[21],"prediction":[22],"which":[23,73],"cannot":[24],"work":[25],"well":[26],"for":[27],"realistic":[29],"application":[30],"scene":[31],"since":[32],"a":[33,47,75,85,102,155],"review":[34,59],"usually":[35],"contain":[36],"multiple":[37],"categories.":[39],"Recent":[40],"attempts":[41],"alleviate":[42],"this":[43,81,95],"issue":[44],"by":[45],"setting":[46],"threshold.":[48],"However,":[49],"imbalance":[51],"of":[52,55,104,127,138],"number":[54],"category-related":[56],"words":[57,129],"segments":[60],"makes":[61],"these":[62],"difficult":[64],"find":[66],"optimal":[67],"thresholds":[68],"recall":[70],"all":[71],"categories,":[72],"is":[74,99],"common":[76],"but":[77],"neglected":[78],"phenomenon.":[79],"In":[80],"paper,":[82],"we":[83,153],"propose":[84],"novel":[86,156],"method":[88],"termed":[89],"Aspect-Category":[90],"Experts":[91,120],"(ACEs)":[92],"address":[94],"problem.":[96],"Our":[97],"goal":[98],"train":[101],"set":[103],"aspect-category":[105],"experts":[106,114,147],"encode":[108],"sentence":[110],"parallel,":[112],"where":[113],"and":[115],"correspond":[118],"one-to-one.":[119],"different":[122,146],"aspects":[123],"weight":[124],"embedding":[126],"representative":[128],"with":[130],"aspect-specific":[131],"attention":[132],"avoid":[134],"negative":[136],"impact":[137],"accumulation.":[139],"Besides,":[140],"enhance":[142],"complementarity":[144],"between":[145],"reduce":[149],"inter-class":[150],"feature":[151],"entanglement,":[152],"construct":[154],"mutual":[157],"exclusion":[158],"loss":[159],"(ME":[160],"loss)":[161],"improve":[163],"performance.":[167],"Extensive":[168],"experimental":[169],"results":[170],"four":[172],"datasets":[173],"demonstrate":[174],"that":[175],"our":[176],"proposed":[177],"ACEs":[178],"model":[179],"outperforms":[180],"previous":[182],"state-of-the-art":[183],"methods.":[184]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}