SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The role of domain information in Word Sense Disambiguation
Natural Language Engineering
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
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
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Getting serious about the development of computational humor
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
From humor recognition to irony detection: The figurative language of social media
Data & Knowledge Engineering
On the Transdisciplinary Field of Humor Research
Journal of Integrated Design & Process Science
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Incongruity resolution is one of the most widely accepted theories of humour, suggesting that humour is due to the mixing of two disparate interpretation frames in one statement. In this paper, we explore several computational models for incongruity resolution. We introduce a new data set, consisting of a series of ‘set-ups' (preparations for a punch line), each of them followed by four possible coherent continuations out of which only one has a comic effect. Using this data set, we redefine the task as the automatic identification of the humorous punch line among all the plausible endings. We explore several measures of semantic relatedness, along with a number of joke-specific features, and try to understand their appropriateness as computational models for incongruity detection.