Abstract
Jokes classification is an intrinsically subjective and complex task, mainly due to the difficulties related to cope with contextual constraints on classifying each joke. Nowadays people have less time to devote to search and enjoy humour and, as a consequence, people are usually interested on having a set of interesting filtered jokes that could be worth reading, that is with a high probability of make them laugh.
In this paper we propose a crowdsourcing based collective intelligent mechanism to classify humour and to recommend the most interesting jokes for further reading. Crowdsourcing is becoming a model for problem solving, as it revolves around using groups of people to handle tasks traditionally associated with experts or machines.
We put forward an active learning Support Vector Machine (SVM) approach that uses crowdsourcing to improve classification of user custom preferences. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brabham, D.C.: Crowdsourcing as a Model for Problem Solving: An Introduction and Cases. Convergence: The International Journal of Research into New Media Technologies 14(1), 75–90 (2008)
Raykar, V., Yu, S., Zhao, L., Valadez, G., Florin, C., Bogoni, L., Moy, L.: Learning from crowds. The Journal of Machine Learning Research 99, 1297–1322 (2010)
Mihalcea, R., Strapparava, C.: Making computers laugh: investigations in automatic humor recognition. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 531–538 (2005)
Baram, Y., El-Yaniv, R., Luz, K.: Online choice of active learning algorithms. In: Proceedings of ICML 2003, 20th International Conference on Machine Learning, pp. 19–26 (2003)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1999)
Joachims, T.: Learning Text Classifiers with Support Vector Machines. Kluwer Academic Publishers, Dordrecht (2002)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research 2, 45–66 (2002)
Antunes, M., Silva, C., Ribeiro, B., Correia, M.: A Hybrid AIS-SVM Ensemble Approach for Text Classification. In: Dobnikar, A., Lotrič, U., Šter, B. (eds.) ICANNGA 2011, Part II. LNCS, vol. 6594, pp. 342–352. Springer, Heidelberg (2011)
Mihalcea, R., Strapparava, C.: Technologies That Make You Smile: Adding Humor to Text-Based Applications. IEEE Intelligent Systems 21(5), 33–39 (2006)
Howe, J.: The Rise of Crowdsourcing. Wired (June 2006)
Hsueh, P.-Y., Melville, P., Sindhwani, V.: Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria, pp. 1–9 (May 2009)
Nov, O., Arazy, O., Anderson, D.: Dusting for science: motivation and participation of digital citizen science volunteers. In: Proceedings of the 2011 iConference, pp. 68–74 (2011)
Surowiecki, J.: The Wisdom of Crowds. Doubleday (2004)
Greengard, S.: Following the crowd. Communications of the ACM 54(2), 20 (2011)
Leimeister, J.: Collective Intelligence. In: Business & Information Systems Engineering, pp. 1–4 (2010)
Tarasov, A., Delany, S.: Using crowdsourcing for labelling emotional speech assets. In: ECAI - Prestigious Applications of Intelligent Systems, pp. 1–11 (2010)
Welinder, P., Perona, P.: Online crowdsourcing: rating annotators and obtaining cost-effective labels. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 25–32 (2010)
Chen, Y., Hsu, W., Liao, H.: Learning facial attributes by crowdsourcing in social media. In: WWW 2011, pp. 25–26 (2011)
Brew, A., Greene, D., Cunnigham, P.: The interaction between supervised learning and crowdsourcing. In: NIPS 2010 (2010)
Stock, O., Strapparava, C.: Getting serious about the development of computational humor. In: IJCAI 2003, pp. 59–64 (2003)
Binsted, K., Ritchie, G.: An implemented model of punning riddles. arXiv.org, vol. cmp-lg (June 1994)
Reyes, A., Potthast, M., Rosso, P., Stein, B.: Evaluating Humor Features on Web Comments. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation, LREC 2010 (May 2010)
Settles, B.: Active learning literature survey. CS Technical Report 1648, University of Wisconsin-Madison (2010)
Silva, C., Ribeiro, B.: On text-based mining with active learning and background knowledge using svm. Soft Computing - A Fusion of Foundations, Methodologies and Applications 11(6), 519–530 (2007)
van Rijsbergen, C.: Information Retrieval. Butterworths ed. (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Costa, J., Silva, C., Antunes, M., Ribeiro, B. (2011). Get Your Jokes Right: Ask the Crowd. In: Bellatreche, L., Mota Pinto, F. (eds) Model and Data Engineering. MEDI 2011. Lecture Notes in Computer Science, vol 6918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24443-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-24443-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24442-1
Online ISBN: 978-3-642-24443-8
eBook Packages: Computer ScienceComputer Science (R0)