Estimation of conditional probabilities with decision trees and an application to fine-grained POS tagging

Authors:
Helmut Schmid;Florian Laws
Affiliations:
University of Stuttgart;University of Stuttgart
Venue:
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Year:
2008

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Abstract

We present a HMM part-of-speech tagging method which is particularly suited for POS tagsets with a large number of fine-grained tags. It is based on three ideas: (1) splitting of the POS tags into attribute vectors and decomposition of the contextual POS probabilities of the HMM into a product of attribute probabilities, (2) estimation of the contextual probabilities with decision trees, and (3) use of high-order HMMs. In experiments on German and Czech data, our tagger outperformed state-of-the-art POS taggers.