MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
CorMet: a computational, corpus-based conventional metaphor extraction system
Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Making computers laugh: investigations in automatic humor recognition
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language 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
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Automatic metaphor interpretation as a paraphrasing task
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Recall-oriented learning of named entities in Arabic Wikipedia
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Special questions and techniques
IBM Journal of Research and Development
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Humor identification is a hard natural language understanding problem. We identify a subproblem --- the "that's what she said" problem --- with two distinguishing characteristics: (1) use of nouns that are euphemisms for sexually explicit nouns and (2) structure common in the erotic domain. We address this problem in a classification approach that includes features that model those two characteristics. Experiments on web data demonstrate that our approach improves precision by 12% over baseline techniques that use only word-based features.