Overview
- Detailed explanation of the Entropy Guided Transformation Learning algorithm
- Detailed explanation of how to create ensembles of ETL classifiers
- Explains how to apply ETL to four NLP problems
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (9 chapters)
-
Front Matter
-
Entropy Guided Transformation Learning: Algorithms
-
Front Matter
-
-
Entropy Guided Transformation Learing: Applications
-
Front Matter
-
-
Back Matter
Keywords
About this book
Entropy Guided Transformation Learning: Algorithms and Applications (ETL) presents a machine learning algorithm for classification tasks. ETL generalizes Transformation Based Learning (TBL) by solving the TBL bottleneck: the construction of good template sets. ETL automatically generates templates using Decision Tree decomposition.
The authors describe ETL Committee, an ensemble method that uses ETL as the base learner. Experimental results show that ETL Committee improves the effectiveness of ETL classifiers. The application of ETL is presented to four Natural Language Processing (NLP) tasks: part-of-speech tagging, phrase chunking, named entity recognition and semantic role labeling. Extensive experimental results demonstrate that ETL is an effective way to learn accurate transformation rules, and shows better results than TBL with handcrafted templates for the four tasks. By avoiding the use of handcrafted templates, ETL enables the use of transformation rules to a greater range of tasks.
Suitable for both advanced undergraduate and graduate courses, Entropy Guided Transformation Learning: Algorithms and Applications provides a comprehensive introduction to ETL and its NLP applications.
Authors and Affiliations
Bibliographic Information
Book Title: Entropy Guided Transformation Learning: Algorithms and Applications
Authors: Cícero Nogueira Santos, Ruy Luiz Milidiú
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-1-4471-2978-3
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2012
Softcover ISBN: 978-1-4471-2977-6Published: 16 March 2012
eBook ISBN: 978-1-4471-2978-3Published: 14 March 2012
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 78
Number of Illustrations: 10 b/w illustrations
Topics: Pattern Recognition, Natural Language Processing (NLP), Computational Linguistics