The language of humour
Humour is one of the most interesting and puzzling aspects of human behaviour. Despite the attention it has received from fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humour recognition and analysis. In this thesis, I use corpus-based approaches to formulate and test hypotheses concerned with the processing of verbal humour. The thesis makes two important contributions. First, it brings empirical evidence that computational approaches can be successfully applied to the task of humour recognition. Through experiments performed on very large data sets, I show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, using content-based features or models of incongruity. Moreover, using a method for measuring feature saliency, I identify and validate several dominant word classes that can be used to characterize humorous text. Second, the thesis provides corpus-based support toward the validity of previously formulated linguistic theories, indicating that humour is primarily due to incongruity and humour-specific language. Experiments performed on collections of verbal
Mihalcea, Rada
Computational Linguistics ; Natural Language Processing ; Linguistics ; Humour Research ; humour research ; humour recognition ; computational humour
The language of humour. Humour is one of the most interesting and puzzling aspects of human behaviour. Despite the attention it has received from fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humour recognition and analysis. In this thesis, I use corpus-based approaches to formulate and test hypotheses concerned with the processing of verbal humour. The thesis makes two important contributions. First, it brings empirical evidence that computational approaches can be successfully applied to the task of humour recognition. Through experiments performed on very large data sets, I show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, using content-based features or models of incongruity. Moreover, using a method for measuring feature saliency, I identify and validate several dominant word classes that can be used to characterize humorous text. Second, the thesis provides corpus-based support toward the validity of previously formulated linguistic theories, indicating that humour is primarily due to incongruity and humour-specific language. Experiments performed on collections of verbal
language humour humour interesting puzzling aspect human_behaviour despite attention receive field philosophy linguistics_psychology attempt_create computational_model humour recognition analysis use corpus_based approach formulate test_hypothesis concerned process verbal humour make two_important contribution first bring empirical_evidence computational approach successfully apply task humour recognition perform large data_set show automatic classification technique effectively use distinguish humorous non humorous text use content based feature model moreover use method_measure feature identify validate several dominant word_class use characterize humorous text second provide corpus_based support toward validity previously formulate linguistic_theory indicate humour primarily due humour specific language perform collection verbal