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 humour show that both incongruity and content-based features can be successfully used to model humour, and that these features are even more effective when used in tandem.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:530060 |
| Date | January 2010 |
| Creators | Mihalcea, Rada |
| Contributors | Pulman, Stephen |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:a1cc3d1e-cc83-44dd-a2dd-6910fde3d252 |
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