ICANNGA 2013: Adaptive and Natural Computing Algorithms pp 90-99 | Cite as
PSO-Tagger: A New Biologically Inspired Approach to the Part-of-Speech Tagging Problem
Abstract
In this paper we present an approach to the part-of-speech tagging problem based on particle swarm optimization. The part-of-speech tagging is a key input feature for several other natural language processing tasks, like phrase chunking and named entity recognition. A tagger is a system that should receive a text, made of sentences, and, as output, should return the same text, but with each of its words associated with the correct part-of-speech tag. The task is not straightforward, since a large percentage of words have more than one possible part-of-speech tag, and the right choice is determined by the part-of-speech tags of the surrounding words, which can also have more than one possible tag. In this work we investigate the possibility of using a particle swarm optimization algorithm to solve the part-of-speech tagging problem supported by a set of disambiguation rules. The results we obtained on two different corpora are amongst the best ones published for those corpora.
Keywords
Part-of-speech Tagging Disambiguation Rules Evolutionary Algorithms Particle Swarm Optimization Natural Language ProcessingPreview
Unable to display preview. Download preview PDF.
References
- 1.Brants, T.: Tnt: a statistical part-of-speech tagger. In: Proceedings of the Sixth Conference on Applied Natural Language Processing, ANLC 2000, pp. 224–231. Association for Computational Linguistics, Stroudsburg (2000)CrossRefGoogle Scholar
- 2.Araujo, L.: Part-of-Speech Tagging with Evolutionary Algorithms. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 230–239. Springer, Heidelberg (2002)CrossRefGoogle Scholar
- 3.Araujo, L., Luque, G., Alba, E.: Metaheuristics for Natural Language Tagging. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 889–900. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 4.Alba, E., Luque, G., Araujo, L.: Natural language tagging with genetic algorithms. Inf. Process. Lett. 100(5), 173–182 (2006)MathSciNetMATHCrossRefGoogle Scholar
- 5.Brill, E.: Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput. Linguist. 21, 543–565 (1995)Google Scholar
- 6.Wilson, G., Heywood, M.: Use of a genetic algorithm in brill’s transformation-based part-of-speech tagger. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 2067–2073. ACM, New York (2005)CrossRefGoogle Scholar
- 7.Nogueira dos Santos, C., Milidiú, R.L., Rentería, R.P.: Portuguese Part-of-Speech Tagging Using Entropy Guided Transformation Learning. In: Teixeira, A., de Lima, V.L.S., de Oliveira, L.C., Quaresma, P. (eds.) PROPOR 2008. LNCS (LNAI), vol. 5190, pp. 143–152. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 8.Steven Bird, E.K., Loper, E.: Natural Language Processing with Python. O’Reilly Media (2009)Google Scholar
- 9.Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. App., 4:1–4:10 (January 2008)Google Scholar
- 10.Kennedy, J., Eberhart, R.C.: Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
- 11.Hindle, D.: Acquiring disambiguation rules from text (1989)Google Scholar