Abstract
In this work we describe a statistical morphological tagger for Latvian, Lithuanian and Estonian languages based on morphological tag disambiguation. These languages have rich tagsets and very high rates of morphological ambiguity. We model distribution of possible tags with an exponential probabilistic model, which allows to select and use features from surrounding context. Results show significant improvement in error rates over the baseline, the same as the results for Czech. In comparison with the simplified parameter estimation method applied for Czech, we show that maximum entropy weight estimation achieves considerably better results.
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Pinnis, M., Goba, K. (2011). Maximum Entropy Model for Disambiguation of Rich Morphological Tags. In: Mahlow, C., Piotrowski, M. (eds) Systems and Frameworks for Computational Morphology. SFCM 2011. Communications in Computer and Information Science, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23138-4_2
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DOI: https://doi.org/10.1007/978-3-642-23138-4_2
Publisher Name: Springer, Berlin, Heidelberg
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