Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-24T04:08:02.225Z Has data issue: false hasContentIssue false
  • English
  • Français

Computational generation and dissection of lexical replacement humor*

Published online by Cambridge University Press:  16 April 2015

ALESSANDRO VALITUTTI ,
ANTOINE DOUCET ,
JUKKA M. TOIVANEN  and
HANNU TOIVONEN
ALESSANDRO VALITUTTI
Affiliation:
Department of Computer Science, University College Dublin, Belfield, Dublin, Ireland e-mail: [email protected]
ANTOINE DOUCET
Affiliation:
L3i Laboratory, University of La Rochelle, avenue M. Crépeau, 17000 La Rochelle, France e-mail: [email protected]
JUKKA M. TOIVANEN
Affiliation:
Department of Computer Science and HIIT, PO Box 68, FI-00014 University of Helsinki, Finland e-mail: [email protected], [email protected]
HANNU TOIVONEN
Affiliation:
Department of Computer Science and HIIT, PO Box 68, FI-00014 University of Helsinki, Finland e-mail: [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We consider automated generation of humorous texts by substitution of a single word in a given short text. In this setting, several factors that potentially contribute to the funniness of texts can be integrated into a unified framework as constraints on the lexical substitution. We discuss three types of such constraints: formal constraints concerning the similarity of sounds or spellings between the original word and the substitute, semantic or connotational constraints requiring the substitute to be a taboo word, and contextual constraints concerning the position and context of the replacement. Empirical evidence from extensive user studies using real SMSs as the corpus indicates that taboo constraints are statistically very effective, and so is a constraint requiring that the substitution takes place at the end of the text even though the effect is smaller. The effects of individual constraints are largely cumulative. In addition, connotational taboo words and word position have a strong interaction.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 5 , September 2016 , pp. 727 - 749
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

We would like to thank the anonymous reviewers for their insightful comments that have greatly helped us improve the paper. This work has been supported by the Academy of Finland (decision 276897, CLiC; and the Algorithmic Data Analysis Centre of Excellence, Algodan), and by the European Commission (FET grant 611733, ConCreTe; and FET grant 611560, WHIM).

References

Beattie, J. 1971. An essay on laughter, and ludicrous composition. In Essays. Reprinted by Garland (Original work published by William Creech, Edinburgh, 1776), New York: Garland Publishing.Google Scholar
Binsted, K., Pain, H., and Ritchie, G., 1997. Children’s evaluation of computer-generated punning riddles. Pragmatics and Cognition 2 (5): 305–354.Google Scholar
Carrell, A., 1997. Joke competence and humor competence. Humor 10 : 173–185.Google Scholar
Chen, T., and Kan, M.-Y., 2013. Creating a live, public short message service corpus: the NUS SMS Corpus. Language Resources and Evaluation 74 (2): 299–335.Google Scholar
Cory, M., 1995. Comedic distance in holocaust literature. Journal of American Culture 18 (1): 35–40.Google Scholar
Doucet, A., and Ahonen-Myka, H., 2006. Probability and expected document frequency of discontinued word sequences, an efficient method for their exact computation. Traitement Automatique des Langues (TAL) 46 (2): 13–37.Google Scholar
Dybala, P., Ptaskynsky, M., Higuchi, S., Rzepka, R., and Araki, K. 2008. Humor Prevails! - Implementing a joke generator into a conversational system. In Proceedings of the 21st Australian Joint Conference on AI (AI-08), vol. 5360, pp. 214–225. Berlin: Springer Verlag.Google Scholar
Fellbaum, C., 1998. WordNet. An Electronic Lexical Database. Cambridge, Massachusetts: The MIT Press.Google Scholar
Hempelmann, C. F., 2003. Paronomasic Puns: Target Recoverability Towards Automatic Generation. Ph.D. thesis, West Lafayette, IN: Purdue University.Google Scholar
Hempelmann, C., Taylor, J., and Raskin, V. 2012. Tightening up joke structure: not by length alone. In Proceedings of the 34th Annual Meeting of the Cognitive Science Society 2012 (CogSci 2012), Sapporo, Japan.Google Scholar
Jay, T., Caldwell-Harris, C., and King, K., 2008. Recalling taboo and nontaboo words. American Journal of Psychology 121 (1): 83–103.Google Scholar
Kazai, G., Kamps, J., Koolen, M., and Milic-Frayling, N. 2011. Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, ACM, pp. 205–214, Beijing, China.Google Scholar
Koestler, A., 1964. The Act of Creation. London: Hutchinson.Google Scholar
Leach, E. 1964. Antropological aspects of language: animal categories and verbal abuse. In Lenneberg, E. H. (ed.), New Directions in the Study of Language, pp. 23–63. Cambridge, Massachusetts: The MIT Press.Google Scholar
Levenshtein, V. I., 1966. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10 (8): 707–710.Google Scholar
Levison, M., and Lessard, G., 1992. A system for natural language generation. Computers and the Humanities 26 : 43–58.Google Scholar
Magnini, B., and Cavaglià, G. 2000. Integrating subject field codes into WordNet. In Proceedings of the 2nd International Conference on Language Resources and Evaluation (LREC2000), pp. 1413–1418, Athens, Greece.Google Scholar
Martin, R. A., 2007. The Psychology of Humor: An Integrative Approach. Elsevier, Elsevier: San Diego, California.Google Scholar
McKay, J. 2002. Generation of idiom-based witticisms to aid second language learning. In Stock, O., Strapparava, C., and Nijholt, A., (eds.), Proceedings of the The April Fools Day Workshop on Computational Humour (TWLT20), pp. 77–87, Trento, Italy.Google Scholar
Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The Google Books Team, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M. A. and Aiden, E. L., 2011. Quantitative analysis of culture using millions of digitized books. Science 331 (6014): 176–182.Google Scholar
Morreall, J. 2013. Philosophy of Humor. In Zalta, E. N. (ed.), The Stanford Encyclopedia of Philosophy. The Metaphysics Research Lab Publisher, Stanford, California.Google Scholar
Mulkay, M., 1988. On Humour: Its Nature and its Place in Modern Society. Cambridge, UK: Polity Press.Google Scholar
Özbal, G., and Strapparava, C. 2012. A computational approach to the automation of creative naming. In Proceedings of the 50th annual meeting of the Association of Computational Linguistics (ACL-2012), pp. 703–711, Jeju Island, South Korea.Google Scholar
Petrović, S., and Matthews, D. 2013. Unsupervised joke generation from big data. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), pp. 228–232, Sofia, Bulgaria.Google Scholar
Raskin, V., 1985. Semantic Mechanisms of Humor. Netherlands: Dordrecht-Boston-Lancaster.Google Scholar
Raskin, V., and Attardo, S., 1994. Non-literalness and non-bona-fide in language: approaches to formal and computational treatments of humor. Pragmatics and Cognition 2 (1): 31–69.Google Scholar
Ritchie, G. 2002. The structure of forced reinterpretation jokes. In Proceedings of the The April Fools Day Workshop on Computational Humour (TWLT20), pp. 47–56, Trento, Italy.Google Scholar
Ritchie, G., 2003. The Linguistic Analysis of Jokes. London: Routledge.Google Scholar
Ross, J., Irani, I., Silberman, M. S., Zaldivar, A., and Tomlinson, B. 2010. Who are the crowdworkers?: shifting demographics in Amazon Mechanical Turk. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 2863–2872, Atlanta, Georgia.Google Scholar
Ruch, W. 1992. Assessment of appreciation of humor: studies with the 3 WD Humor Test. In Spielberger, C. D. and Butcher, J. N. (eds.), Advances in Personality Assessment, vol. 9, pp. 27–75. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Ruch, W. 2008. Psychology of humor. In Raskin, V. (ed.), The Primer of Humor Research, pp. 17–100. De Gruyter Mouton, Hillsdale, New Jersey.Google Scholar
Schank, R., and Abelson, R. 1977. Scripts, Plans Goals and Understanding: An Inquiry into Human Knowledge Structures. Erlbaum, Hillsdale, NJ.Google Scholar
Seizer, S., 2011. On the uses of obscenity in live stand-up comedy. Anthropological Quarterly 84 (1): 209–234.Google Scholar
Sherzer, J. 2002. Speech Play and Verbal Art. University of Texas Press, Austin, Texas.Google Scholar
Sjöbergh, J. 2006. Vulgarities are fucking funny, or at least make things a little bit funnier. Technical Report TRITA-CSC-TCS 2006: 4, School of Computer Science and Communication, the Royal Institute of Technology, Stockholm.Google Scholar
Stock, O., and Strapparava, C. 2003. HAHAcronym: humorous agents for humorous acronyms. Humor: International Journal of Humor Research 16 (3), pp. 297–314.Google Scholar
Suls, J. 1972. A two-stage model for the appreciation of jokes and cartoons: an information-processing analysis. In Goldstein, J. and McGhee, P. (ed.), The Psychology of Humor, pp. 81–100. New York: Academic Press.Google Scholar
Taylor, J., and Mazlack, L. 2005. Toward computational recognition of humorous intent. In Proceedings of the 27th Annual Conference of the Cognitive Science Society (COGSCI 05), pp. 2166–2171, Stresa, Italy.Google Scholar
Valitutti, A. 2011. How many jokes are really funny? Towards a new approach to the evaluation of computational humour generators. In Proceedings of 8th International Workshop on Natural Language Processing and Cognitive Science, pp. 189–200, Copenhagen, Denmark.Google Scholar
Valitutti, A., Toivonen, H., Doucet, A., and Toivanen, J. M. 2013. ‘Let everything turn well in your wife’: generation of adult humor using lexical constraints. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), pp. 243–248, Sofia, Bulgaria.Google Scholar
Veale, T., 2013. Humorous similes. HUMOR: The International Journal of Humor Research 21 (1): 3–22.Google Scholar
Venour, C. 1999. The computational generation of a class of puns. Master’s thesis, Kingston, Ontario: Queen’s University.Google Scholar
Westfall, P. H., and Young, S. S., 1993. Resampling-Based Multiple Testing. New York: John Wiley & Sons.Google Scholar
Zwicky, A. M., 1979. Classical malapropisms. Language Sciences 1 (2): 339–348.Google Scholar