{"id":"https://openalex.org/W2250480277","doi":"https://doi.org/10.18653/v1/d15-1116","title":"Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words","display_name":"Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2250480277","doi":"https://doi.org/10.18653/v1/d15-1116","mag":"2250480277"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d15-1116","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1116","pdf_url":"https://www.aclweb.org/anthology/D15-1116.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 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D15-1116.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101867956","display_name":"Debanjan Ghosh","orcid":"https://orcid.org/0000-0001-7220-9928"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Debanjan Ghosh","raw_affiliation_strings":["School of Communication and Information, Rutgers University, NJ, USA"],"affiliations":[{"raw_affiliation_string":"School of Communication and Information, Rutgers University, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027639981","display_name":"Weiwei Guo","orcid":"https://orcid.org/0000-0001-5037-0972"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weiwei Guo","raw_affiliation_strings":["Department of Computer Science, Columbia University, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Columbia University, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043262011","display_name":"Smaranda Muresan","orcid":"https://orcid.org/0000-0003-4532-0182"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Smaranda Muresan","raw_affiliation_strings":["Center for Computational Learning Systems, Columbia University, NY, USA"],"affiliations":[{"raw_affiliation_string":"Center for Computational Learning Systems, Columbia University, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101867956"],"corresponding_institution_ids":["https://openalex.org/I102322142"],"apc_list":null,"apc_paid":null,"fwci":12.9077,"has_fulltext":true,"cited_by_count":115,"citation_normalized_percentile":{"value":0.98659947,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9993000030517578,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9993000030517578,"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":0.9980999827384949,"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":0.9980999827384949,"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/literal","display_name":"Literal (mathematical logic)","score":0.713489294052124},{"id":"https://openalex.org/keywords/meaning","display_name":"Meaning (existential)","score":0.681050181388855},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6541379690170288},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.600947380065918},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.57973712682724},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5524243712425232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5465911626815796},{"id":"https://openalex.org/keywords/sarcasm","display_name":"Sarcasm","score":0.5160210132598877},{"id":"https://openalex.org/keywords/literal-and-figurative-language","display_name":"Literal and figurative language","score":0.5046554803848267},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3168853521347046},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.10645341873168945},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.0904022753238678}],"concepts":[{"id":"https://openalex.org/C2780882242","wikidata":"https://www.wikidata.org/wiki/Q14235582","display_name":"Literal (mathematical logic)","level":2,"score":0.713489294052124},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.681050181388855},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6541379690170288},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.600947380065918},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.57973712682724},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5524243712425232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5465911626815796},{"id":"https://openalex.org/C2776207355","wikidata":"https://www.wikidata.org/wiki/Q191035","display_name":"Sarcasm","level":3,"score":0.5160210132598877},{"id":"https://openalex.org/C46182478","wikidata":"https://www.wikidata.org/wiki/Q7363315","display_name":"Literal and figurative language","level":2,"score":0.5046554803848267},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3168853521347046},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.10645341873168945},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0904022753238678},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/d15-1116","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1116","pdf_url":"https://www.aclweb.org/anthology/D15-1116.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 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.696.2753","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.2753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://aclweb.org/anthology/D/D15/D15-1116.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/d15-1116","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1116","pdf_url":"https://www.aclweb.org/anthology/D15-1116.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 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2250480277.pdf","grobid_xml":"https://content.openalex.org/works/W2250480277.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1525961042","https://openalex.org/W1593045043","https://openalex.org/W1614298861","https://openalex.org/W1615991656","https://openalex.org/W2041400887","https://openalex.org/W2045929671","https://openalex.org/W2096463805","https://openalex.org/W2107130271","https://openalex.org/W2114661483","https://openalex.org/W2124807415","https://openalex.org/W2126725946","https://openalex.org/W2135875128","https://openalex.org/W2143927888","https://openalex.org/W2153579005","https://openalex.org/W2153635508","https://openalex.org/W2157765050","https://openalex.org/W2158899491","https://openalex.org/W2165044314","https://openalex.org/W2250489604","https://openalex.org/W2250539671","https://openalex.org/W2250710744","https://openalex.org/W2251874715","https://openalex.org/W2251920663","https://openalex.org/W2251958472","https://openalex.org/W2277095315","https://openalex.org/W2489406233","https://openalex.org/W2595715041","https://openalex.org/W2882319491","https://openalex.org/W2950577311","https://openalex.org/W2952230511","https://openalex.org/W3120421331","https://openalex.org/W4233787372","https://openalex.org/W4294170691"],"related_works":["https://openalex.org/W2733869487","https://openalex.org/W4312945876","https://openalex.org/W2498629578","https://openalex.org/W2993406133","https://openalex.org/W2393137124","https://openalex.org/W3013347530","https://openalex.org/W2062511441","https://openalex.org/W2323021101","https://openalex.org/W3088098373","https://openalex.org/W4312729040"],"abstract_inverted_index":{"Sarcasm":[0],"is":[1,21,50,105],"generally":[2],"characterized":[3],"as":[4,36],"a":[5,14,17,37,48,72,94,126,133,140,145],"figure":[6],"of":[7,13,25,39,47,74],"speech":[8],"that":[9,77,125],"involves":[10],"the":[11,23,26,32,45,58,102,108,111,115],"substitution":[12],"literal":[15,28,52,81,109],"by":[16],"figurative":[18],"meaning,":[19],"which":[20],"usually":[22],"opposite":[24],"original":[27],"meaning.":[29],"We":[30,55,64],"re-frame":[31],"sarcasm":[33],"detection":[34],"task":[35],"type":[38],"word":[40,49,104,137],"sense":[41,46],"disambiguation":[42],"problem,":[43],"where":[44],"either":[51,80],"or":[53,82,110],"sarcastic.":[54],"call":[56],"this":[57],"Literal/Sarcastic":[59],"Sense":[60],"Disambiguation":[61],"(LSSD)":[62],"task.":[63],"address":[65],"two":[66],"issues:":[67],"1)":[68],"how":[69,97],"to":[70,98],"collect":[71],"set":[73],"target":[75,95,103],"words":[76],"can":[78],"have":[79],"sarcastic":[83,112],"meanings":[84],"depending":[85],"on":[86],"context;":[87],"and":[88,93,123],"2)":[89],"given":[90],"an":[91],"utterance":[92],"word,":[96],"automatically":[99],"detect":[100],"whether":[101],"used":[106],"in":[107],"sense.":[113],"For":[114],"latter,":[116],"we":[117],"investigate":[118],"several":[119],"distributional":[120],"semantics":[121],"methods":[122],"show":[124],"Support":[127],"Vector":[128],"Machines":[129],"(SVM)":[130],"classifier":[131],"with":[132],"modified":[134],"kernel":[135],"using":[136],"embeddings":[138],"achieves":[139],"7-10%":[141],"F1":[142],"improvement":[143],"over":[144],"strong":[146],"lexical":[147],"baseline.":[148]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":25},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}