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Entropy Guided Transformation Learning: Algorithms and Applications

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

This chapter presents a brief introduction to entropy guided transformation learning (ETL), a machine learning algorithm for classification tasks. ETL generalizes transformation based learning (TBL) by automatically solving the TBL bottleneck: the construction of good template sets. The main advantage of ETL is its easy applicability to natural language processing (NLP) tasks. This introductory chapter presents the motivation behind ETL and summarizes our experimental results. In Sect. 1.1, we first briefly detail TBL and explain its bottleneck. Next, we briefly present ETL and list some of its advantages. In Sect. 1.2, we first list some related works on the use of ETL for different NLP tasks. Next, we report a summary of our experimental results on the application of ETL to four language independent NLP tasks: part-of-speech tagging, phrase chunking, named entity recognition and semantic role labeling. Finally, in Sect. 1.3, we detail the structure of the book.

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Authors and Affiliations

  1. Research, IBM Research Brazil, Av. Pasteur 146, Rio de Janeiro, RJ, 22296-903, Brazil

    Cícero Nogueira dos Santos

  2. Departamento de Informática (DI), Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil

    Ruy Luiz Milidiú

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  1. Cícero Nogueira dos Santos
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  2. Ruy Luiz Milidiú
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dos Santos, C.N., Milidiú, R.L. (2012). Introduction. In: Entropy Guided Transformation Learning: Algorithms and Applications. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-2978-3_1

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  • DOI: https://doi.org/10.1007/978-1-4471-2978-3_1

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  • Online ISBN: 978-1-4471-2978-3

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