{"id":"https://openalex.org/W2913873497","doi":"https://doi.org/10.1145/3308558.3313735","title":"Sarcasm Detection with Self-matching Networks and Low-rank Bilinear Pooling","display_name":"Sarcasm Detection with Self-matching Networks and Low-rank Bilinear Pooling","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2913873497","doi":"https://doi.org/10.1145/3308558.3313735","mag":"2913873497"},"language":"en","primary_location":{"id":"doi:10.1145/3308558.3313735","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313735","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3308558.3313735","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101439585","display_name":"Tao Xiong","orcid":"https://orcid.org/0000-0003-1583-8669"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tao Xiong","raw_affiliation_strings":["Alibaba Group, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017376448","display_name":"Peiran Zhang","orcid":"https://orcid.org/0000-0002-3873-9949"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiran Zhang","raw_affiliation_strings":["Alibaba Group, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110816132","display_name":"Hongbo Zhu","orcid":"https://orcid.org/0000-0001-5429-8226"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbo Zhu","raw_affiliation_strings":["Alibaba Group, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101253915","display_name":"Yihui Yang","orcid":"https://orcid.org/0009-0000-5738-404X"},"institutions":[{"id":"https://openalex.org/I4210098154","display_name":"UnionPay (China)","ror":"https://ror.org/00vg14y34","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210098154"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihui Yang","raw_affiliation_strings":["AfterPay, China"],"affiliations":[{"raw_affiliation_string":"AfterPay, China","institution_ids":["https://openalex.org/I4210098154"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101439585"],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":null,"apc_paid":null,"fwci":5.3534,"has_fulltext":false,"cited_by_count":127,"citation_normalized_percentile":{"value":0.96477193,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2115","last_page":"2124"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","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/T10028","display_name":"Topic Modeling","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/T10181","display_name":"Natural Language Processing Techniques","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":0.8164598941802979},{"id":"https://openalex.org/keywords/sarcasm","display_name":"Sarcasm","score":0.7564009428024292},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6987212896347046},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6323258280754089},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6182356476783752},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6021431684494019},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5951129794120789},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5531376004219055},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.46711453795433044},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4338282644748688},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.42863228917121887}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8164598941802979},{"id":"https://openalex.org/C2776207355","wikidata":"https://www.wikidata.org/wiki/Q191035","display_name":"Sarcasm","level":3,"score":0.7564009428024292},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6987212896347046},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6323258280754089},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6182356476783752},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6021431684494019},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5951129794120789},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5531376004219055},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.46711453795433044},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4338282644748688},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.42863228917121887},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2779975665","wikidata":"https://www.wikidata.org/wiki/Q131361","display_name":"Irony","level":2,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3308558.3313735","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313735","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3308558.3313735","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313735","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6600000262260437,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1516184288","https://openalex.org/W1733704070","https://openalex.org/W1842080548","https://openalex.org/W1968673961","https://openalex.org/W2009578396","https://openalex.org/W2024011160","https://openalex.org/W2038634595","https://openalex.org/W2041400887","https://openalex.org/W2070761441","https://openalex.org/W2077018496","https://openalex.org/W2097726431","https://openalex.org/W2099653665","https://openalex.org/W2114661483","https://openalex.org/W2165044314","https://openalex.org/W2232443784","https://openalex.org/W2250489604","https://openalex.org/W2250539671","https://openalex.org/W2250710744","https://openalex.org/W2251210340","https://openalex.org/W2251379416","https://openalex.org/W2251676327","https://openalex.org/W2251920663","https://openalex.org/W2251958472","https://openalex.org/W2251971374","https://openalex.org/W2252381721","https://openalex.org/W2263859238","https://openalex.org/W2268623141","https://openalex.org/W2463565445","https://openalex.org/W2470673105","https://openalex.org/W2510141903","https://openalex.org/W2512532697","https://openalex.org/W2529281176","https://openalex.org/W2575367545","https://openalex.org/W2741913829","https://openalex.org/W2758985501","https://openalex.org/W2887428522","https://openalex.org/W2895715183","https://openalex.org/W2949197413","https://openalex.org/W2950133940","https://openalex.org/W2962681323","https://openalex.org/W2964126051","https://openalex.org/W3194034633","https://openalex.org/W4205184193"],"related_works":["https://openalex.org/W3128185314","https://openalex.org/W4312945876","https://openalex.org/W1995736631","https://openalex.org/W1835566166","https://openalex.org/W2043075591","https://openalex.org/W2125145484","https://openalex.org/W2541882558","https://openalex.org/W4200241356","https://openalex.org/W4385572842","https://openalex.org/W4307004515"],"abstract_inverted_index":{"Sarcasm":[0],"is":[1,6],"sophisticated":[2],"linguistic":[3],"expression":[4],"and":[5,12,29,45,67,119,136,164,206],"commonly":[7],"observed":[8],"on":[9,41,113,185,197],"social":[10],"media":[11],"e-commerce":[13],"platforms.":[14],"Failure":[15],"to":[16,33,59,63,82,106,129,138,156,173],"detect":[17],"sarcastic":[18],"expressions":[19],"in":[20,98,102],"natural":[21],"language":[22],"processing":[23],"tasks,":[24],"such":[25],"as":[26],"opinion":[27],"mining":[28],"sentiment":[30],"analysis,":[31],"leads":[32],"poor":[34],"model":[35,192],"performance.":[36,72],"Traditional":[37],"approaches":[38],"rely":[39],"heavily":[40],"discrete":[42],"handcrafted":[43],"features":[44],"will":[46],"incur":[47],"enormous":[48],"human":[49],"costs.":[50],"It":[51],"was":[52],"not":[53],"until":[54],"recent":[55],"that":[56,190],"scholars":[57],"began":[58],"employ":[60],"neural":[61],"networks":[62],"address":[64],"these":[65],"limitations":[66],"have":[68],"achieved":[69],"new":[70],"state-of-the-art":[71],"In":[73,91,144],"this":[74],"work,":[75],"we":[76,93,115,146],"propose":[77],"a":[78,108,125,148,168],"novel":[79],"self-matching":[80,109],"network":[81,126,151,155],"capture":[83],"sentence":[84,105,118,128],"\u201dincongruity\u201d":[85],"information":[86,97,163,166,177],"by":[87,134],"exploring":[88],"word-to-word":[89,100],"interactions.":[90],"particular,":[92],"calculate":[94],"the":[95,103,117,139],"joint":[96],"each":[99],"pair":[101],"input":[104],"build":[107,120],"attention":[110],"vector,":[111],"based":[112],"which":[114],"attend":[116],"its":[121],"representation":[122],"vector.":[123],"Such":[124],"allows":[127],"match":[130],"within":[131],"itself":[132],"word":[133,135],"cater":[137],"words":[140],"of":[141],"conflict":[142],"sentiments.":[143],"addition,":[145],"incorporate":[147],"bi-directional":[149],"LSTM":[150],"into":[152],"our":[153,191],"proposed":[154],"retain":[157],"compositional":[158,165],"information.":[159],"We":[160],"concatenate":[161],"incongruity":[162],"through":[167],"Low-rank":[169],"Bilinear":[170],"Pooling":[171],"method":[172],"control":[174],"for":[175],"potential":[176],"redundancy":[178],"without":[179],"losing":[180],"discriminative":[181],"power.":[182],"Experiment":[183],"results":[184],"publicly":[186],"available":[187],"datasets":[188],"demonstrate":[189],"significantly":[193],"outperforms":[194],"extant":[195],"baselines":[196],"standard":[198],"evaluation":[199],"metrics":[200],"including":[201],"precision,":[202],"recall,":[203],"F1":[204],"score":[205],"accuracy.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":36},{"year":2024,"cited_by_count":31},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}