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MaltOptimizer: Fast and effective parser optimization

Published online by Cambridge University Press:  24 February 2014

MIGUEL BALLESTEROS  and
JOAKIM NIVRE
MIGUEL BALLESTEROS
Affiliation:
Natural Language Processing Group, Pompeu Fabra University, Tànger 122-140, 08018 Barcelona, Spain e-mail: [email protected]
JOAKIM NIVRE
Affiliation:
Department of Linguistics and Philology, Uppsala University, Box 635, 75126 Uppsala, Sweden e-mail: [email protected]
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Abstract

Statistical parsers often require careful parameter tuning and feature selection. This is a nontrivial task for application developers who are not interested in parsing for its own sake, and it can be time-consuming even for experienced researchers. In this paper we present MaltOptimizer, a tool developed to automatically explore parameters and features for MaltParser, a transition-based dependency parsing system that can be used to train parser's given treebank data. MaltParser provides a wide range of parameters for optimization, including nine different parsing algorithms, an expressive feature specification language that can be used to define arbitrarily rich feature models, and two machine learning libraries, each with their own parameters. MaltOptimizer is an interactive system that performs parser optimization in three stages. First, it performs an analysis of the training set in order to select a suitable starting point for optimization. Second, it selects the best parsing algorithm and tunes the parameters of this algorithm. Finally, it performs feature selection and tunes machine learning parameters. Experiments on a wide range of data sets show that MaltOptimizer quickly produces models that consistently outperform default settings and often approach the accuracy achieved through careful manual optimization.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 2 , March 2016 , pp. 187 - 213
Copyright
Copyright © Cambridge University Press 2014 

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