Abstract
There are many classification tools that can be used for various NLP tasks, although none of them can be considered the best of all since each one has a particular list of virtues and defects. The combination methods can serve both to maximize the strengths of the base classifiers and to reduce errors caused by their defects improving the results in terms of accuracy. Here is a comparative study on the most relevant methods that shows that combination seems to be a robust and reliable way of improving our results.
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References
Brants, T.: Tnt. a statistical part-of-speech tagger. In: In Proceedings of the 6th Applied NLP Conference ANLP 2000, pp. 224–231 (2000)
Brill, E., Wu, J.: Classifier combination for improved lexical disambiguation. In: Proceedings of the 17th International Conference on Computational Linguistics, pp. 191–195 (1998)
Daelemans, W., Zavrel, J., Berck, P., Gillis, S.: Mbt: A memorybased part of speech tagger-generator. In: Proceedings of the 4th Workshop on Very Large Corpora, pp. 14–27 (1996)
Halteren, H.V., Zavrel, J., Daelemans, W.: Improving data driven wordclass tagging by system combination. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, vol. 1, pp. 491–497 (1998)
Hansen, L., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Joachims, T.: Making large-Scale SVM Learning Practical, ch. 11. MIT Press, Cambridge (1999)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51, 181–207 (2003)
Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the Conference on New Methods in Language Processing (1994)
Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their application to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics 22, 418–435 (1992)
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Enríquez, F., Troyano, J.A., Cruz, F.L., Ortega, F.J. (2011). A Comparative Study of Classifier Combination Methods Applied to NLP Tasks. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_33
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DOI: https://doi.org/10.1007/978-3-642-22327-3_33
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