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
Disjunctor selection for one-line jokes
INTETAIN'05 Proceedings of the First international conference on Intelligent Technologies for Interactive Entertainment
Linguistic Ethnography: Identifying Dominant Word Classes in Text
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
New Frontiers in Artificial Intelligence
An Analysis of the Impact of Ambiguity on Automatic Humour Recognition
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
The impact of semantic and morphosyntactic ambiguity on automatic humour recognition
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
From humor recognition to irony detection: The figurative language of social media
Data & Knowledge Engineering
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We present a machine learning approach for classifying sentences as one-liner jokes or normal sentences. We use no deep analysis of the meaning to try to see if it is humorous, instead we rely on a combination of simple features to see if these are enough to detect humor. Features such as word overlap with other jokes, presence of words common in jokes, ambiguity and word overlap with common idioms turn out to be useful. When training and testing on equal amounts of jokes and sentences from the British National Corpus, a classification accuracy of 85% is achieved.