{"id":"https://openalex.org/W4387814489","doi":"https://doi.org/10.1145/3606039.3613107","title":"Humor Detection System for MuSE 2023: Contextual Modeling, Pesudo Labelling, and Post-smoothing","display_name":"Humor Detection System for MuSE 2023: Contextual Modeling, Pesudo Labelling, and Post-smoothing","publication_year":2023,"publication_date":"2023-10-20","ids":{"openalex":"https://openalex.org/W4387814489","doi":"https://doi.org/10.1145/3606039.3613107"},"language":"en","primary_location":{"id":"doi:10.1145/3606039.3613107","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3606039.3613107","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3606039.3613107","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 4th on Multimodal Sentiment Analysis Challenge and Workshop: Mimicked Emotions, Humour and Personalisation","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3606039.3613107","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003530730","display_name":"Mingyu Xu","orcid":"https://orcid.org/0000-0001-9378-3806"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingyu Xu","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9378-3806","affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075936470","display_name":"Shun Chen","orcid":"https://orcid.org/0009-0004-8448-5507"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shun Chen","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-8448-5507","affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001973434","display_name":"Zheng Lian","orcid":"https://orcid.org/0000-0001-9477-0599"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Lian","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9477-0599","affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100395562","display_name":"Bin Liu","orcid":"https://orcid.org/0000-0003-1529-1552"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Liu","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1529-1552","affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5003530730"],"corresponding_institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":2.9187,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.90868832,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11795","display_name":"Humor Studies and Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11795","display_name":"Humor Studies and Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11309","display_name":"Music and Audio Processing","score":0.9279000163078308,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/smoothing","display_name":"Smoothing","score":0.7534213066101074},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7093127965927124},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6854736804962158},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6682431101799011},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6279634237289429},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5226730704307556},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.497421532869339},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4661584794521332},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.4542929530143738},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.43925637006759644},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4279245138168335},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11603733897209167}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7534213066101074},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7093127965927124},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6854736804962158},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6682431101799011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6279634237289429},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5226730704307556},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.497421532869339},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4661584794521332},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.4542929530143738},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.43925637006759644},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4279245138168335},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11603733897209167},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"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:10.1145/3606039.3613107","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3606039.3613107","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3606039.3613107","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 4th on Multimodal Sentiment Analysis Challenge and Workshop: Mimicked Emotions, Humour and Personalisation","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3606039.3613107","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3606039.3613107","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3606039.3613107","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 4th on Multimodal Sentiment Analysis Challenge and Workshop: Mimicked Emotions, Humour and Personalisation","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8100000023841858,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G1202467192","display_name":null,"funder_award_id":"U21B2010","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1448168285","display_name":null,"funder_award_id":"62276259","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5381013183","display_name":null,"funder_award_id":"61831022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8569810394","display_name":null,"funder_award_id":"62201572","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320318547","display_name":"Baidu","ror":"https://ror.org/03vs3wt56"},{"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/W4387814489.pdf","grobid_xml":"https://content.openalex.org/works/W4387814489.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1785074626","https://openalex.org/W2005418748","https://openalex.org/W2020388131","https://openalex.org/W2021913835","https://openalex.org/W2021935308","https://openalex.org/W2024760831","https://openalex.org/W2108598243","https://openalex.org/W2178225550","https://openalex.org/W2239141610","https://openalex.org/W2531638282","https://openalex.org/W2607045400","https://openalex.org/W2766925079","https://openalex.org/W2804900514","https://openalex.org/W2941932279","https://openalex.org/W2980994438","https://openalex.org/W3015522062","https://openalex.org/W3090041645","https://openalex.org/W3119298692","https://openalex.org/W3155877414","https://openalex.org/W3159481202","https://openalex.org/W3207721564","https://openalex.org/W4214507204","https://openalex.org/W4220785590","https://openalex.org/W4323924189","https://openalex.org/W4387969070","https://openalex.org/W6677096361"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2093471820","https://openalex.org/W50079190","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2347460059","https://openalex.org/W2352448290","https://openalex.org/W3094960827"],"abstract_inverted_index":{"Humor":[0],"detection":[1],"has":[2,21],"emerged":[3],"as":[4],"an":[5,148],"active":[6],"research":[7],"area":[8],"within":[9],"the":[10,16,26,40,49,82,91,98,102,105,115,124,134,154],"field":[11],"of":[12,28,43,51,101,114,126,151,156],"artificial":[13],"intelligence.":[14],"Over":[15],"past":[17],"few":[18],"decades,":[19],"it":[20],"made":[22],"remarkable":[23],"progress":[24],"with":[25,64],"development":[27],"deep":[29],"learning.":[30],"This":[31],"paper":[32],"introduces":[33],"a":[34,70,160],"novel":[35],"framework":[36],"aimed":[37],"at":[38,138],"enhancing":[39],"model's":[41],"understanding":[42],"humorous":[44],"expressions.":[45],"Specifically,":[46],"we":[47,68,80,108,129],"consider":[48],"impact":[50],"correspondence":[52],"between":[53],"labels":[54],"and":[55,117],"features.":[56],"In":[57],"order":[58,95],"to":[59,85,96],"achieve":[60],"more":[61],"effective":[62],"models":[63,158],"limited":[65],"training":[66,116],"samples,":[67],"employ":[69],"widely":[71],"utilized":[72],"semi-supervised":[73],"learning":[74],"technique":[75],"called":[76],"pseudo":[77],"labeling.":[78],"Furthermore,":[79],"use":[81],"post-smoothing":[83],"strategy":[84],"eliminate":[86],"abnormally":[87],"high":[88],"predictions.":[89],"At":[90],"same":[92],"time,":[93],"in":[94],"alleviate":[97],"over-fitting":[99],"phenomenon":[100],"model":[103],"on":[104,133],"validation":[106],"set,":[107],"created":[109],"10":[110],"different":[111],"random":[112],"subsets":[113],"then":[118],"aggregating":[119],"their":[120],"prediction.":[121],"To":[122],"verify":[123],"effectiveness":[125],"our":[127,145],"strategy,":[128],"evaluate":[130],"its":[131],"performance":[132,155],"Cross-Cultural":[135],"Humour":[136],"sub-challenge":[137],"MuSe":[139],"2023.":[140],"Experimental":[141],"results":[142],"demonstrate":[143],"that":[144],"system":[146],"achieves":[147],"AUC":[149],"score":[150],"0.9112,":[152],"surpassing":[153],"baseline":[157],"by":[159],"substantial":[161],"margin.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
