WordNet for Italian and Its Use for Lexical Deiscrimination
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Parsing engineering and empirical robustness
Natural Language Engineering
Technologies That Make You Smile: Adding Humor to Text-Based Applications
IEEE Intelligent Systems
HAHAcronym: a computational humor system
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
The Multidisciplinary Facets of Research on Humour
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Some Experiments in Humour Recognition Using the Italian Wikiquote Collection
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Recognizing Humor Without Recognizing Meaning
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Characterizing Humour: An Exploration of Features in Humorous Texts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
| Hi-index | 0.00 |
One of the most amazing characteristics that defines the human being is humour. Its analysis implies a set of subjective and fuzzy factors, such as the linguistic, psychological or sociological variables that produce it. This is one of the reasons why its automatic processing seems to be not straightforward. However, recent researches in the Natural Language Processing area have shown that humour can automatically be generated and recognised with success. On the basis of those achievements, in this study we present the experiments we have carried out on a collection of Italian texts in order to investigate how to characterize humour through the study of the ambiguity, especially with respect to morphosyntactic and syntactic ambiguity. The results we have obtained show that it is possible to differentiate humorous from non humorous data through features like perplexity or sentence complexity.