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Evolutionary TBL template generation

Abstract

Transformation Based Learning (TBL) is a Machine Learning technique frequently used in some Natural Language Processing (NLP) tasks. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template generation process. Additionally, we report our findings on five experiments with useful NLP tasks. We observe that our approach provides template sets with a mean loss of performance of 0.5% when compared to human built templates

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Correspondence to Ruy Luiz Milidiú.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Milidiú, R.L., Duarte, J.C. & Santos, C.N.d. Evolutionary TBL template generation. J Braz Comp Soc 13, 39–50 (2007). https://doi.org/10.1007/BF03194255

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  • DOI: https://doi.org/10.1007/BF03194255

Keywords

  • Machine Learning
  • Genetic Algorithms
  • Transformation Error-Driven Based Learning