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.
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