The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
WordNet: a lexical database for English
Communications of the ACM
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Humor modeling in the interface
CHI '03 Extended Abstracts on Human Factors in Computing Systems
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Getting serious about the development of computational humor
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The Multidisciplinary Facets of Research on Humour
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Recognizing Humor Without Recognizing Meaning
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Linguistic Ethnography: Identifying Dominant Word Classes in Text
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Characterizing Humour: An Exploration of Features in Humorous Texts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
New Frontiers in Artificial Intelligence
What humour tells us about discourse theories
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Humor: prosody analysis and automatic recognition for F*R*I*E*N*D*S*
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
May all your wishes come true: a study of wishes and how to recognize them
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semi-supervised recognition of sarcastic sentences in Twitter and Amazon
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
That's what she said: double entendre identification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
The importance of precision in humour classification
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Get your jokes right: ask the crowd
MEDI'11 Proceedings of the First international conference on Model and data engineering
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Computational models for incongruity detection in humour
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Learning from bullying traces in social media
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Automatic humor classification on Twitter
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
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Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this paper, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines.