Self-organizing Markov models and their application to part-of-speech tagging

Authors:
Jin-Dong Kim;Hae-Chang Rim;Jun'ich Tsujii
Affiliations:
University of Tokyo;Korea University;University of Tokyo
Venue:
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Year:
2003

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Abstract

This paper presents a method to develop a class of variable memory Markov models that have higher memory capacity than traditional (uniform memory) Markov models. The structure of the variable memory models is induced from a manually annotated corpus through a decision tree learning algorithm. A series of comparative experiments show the resulting models outperform uniform memory Markov models in a part-of-speech tagging task.