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Empirical Comparison of Evaluation Methods for Unsupervised Learning of Morphology

Sami Virpioja*, Ville T. Turunen*, Sebastian Spiegler**, Oskar Kohonen*, Mikko Kurimo*

*Department of Information and Computer Science
Aalto University
P.O. Box 15400
FI-00076 Aalto
Finland
sami.virpioja@aalto.fi

**Department of Computer Science
University of Bristol
Woodland Road
Bristol BS8 1UB
UK
spiegler@cs.bris.ac.uk


Unsupervised and semi-supervised learning of morphology provide practical solutions for processing morphologically rich languages with less human labor than the traditional rule-based analyzers. Direct evaluation of the learning methods using linguistic reference analyses is important for their development, as evaluation through the final applications is often time consuming. However, even linguistic evaluation is not straightforward for full morphological analysis, because the morpheme labels generated by the learning method can be arbitrary. We review the previous evaluation methods for the learning tasks and propose new variations. In order to compare the methods, we perform an extensive meta-evaluation using the large collection of results from the Morpho Challenge competitions.


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Sami Virpioja, Ville T. Turunen, Sebastian Spiegler, Oskar Kohonen, Mikko Kurimo
749.4 ko

TAL Volume 52 2011 . 2. Vers la morphologie et au-delà

Date de dernière mise à jour : 26 mars 2012, auteur : Rédacteurs en chef.