An Analysis of the Impact of Ambiguity on Automatic Humour Recognition

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.

[1]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[2]  Rada Mihalcea,et al.  The Multidisciplinary Facets of Research on Humour , 2007, WILF.

[3]  Alex Acero,et al.  Spoken Language Processing , 2001 .

[4]  Rada Mihalcea,et al.  Characterizing Humour: An Exploration of Features in Humorous Texts , 2009, CICLing.

[5]  Kim Binsted,et al.  Machine humour : an implemented model of puns , 1996 .

[6]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[7]  Carlo Strapparava,et al.  LEARNING TO LAUGH (AUTOMATICALLY): COMPUTATIONAL MODELS FOR HUMOR RECOGNITION , 2006, Comput. Intell..

[8]  Carlo Strapparava,et al.  WordNet for Italian and Its Use for Lexical Deiscrimination , 1997, AI*IA.

[9]  Sushmita Mitra,et al.  Applications of Fuzzy Sets Theory, 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings , 2007, WILF.

[10]  K. Binsted,et al.  Towards a model of story puns , 2001 .

[11]  Paolo Rosso,et al.  Some Experiments in Humour Recognition Using the Italian Wikiquote Collection , 2007, WILF.

[12]  Carlo Strapparava,et al.  Technologies That Make You Smile: Adding Humor to Text-Based Applications , 2006, IEEE Intelligent Systems.

[13]  Helmut Schmid,et al.  Improvements in Part-of-Speech Tagging with an Application to German , 1999 .

[14]  Kenji Araki,et al.  Recognizing Humor Without Recognizing Meaning , 2007, WILF.

[15]  Carlo Strapparava,et al.  HAHAcronym: A Computational Humor System , 2005, ACL.

[16]  K. Binsted,et al.  Computational rules for generating punning riddles , 1997 .

[17]  Roberto Basili,et al.  Parsing engineering and empirical robustness , 2002, Natural Language Engineering.