Self-Organizing Markov Models and Their Application to Part-of-Speech Tagging

Jin-Dong Kim, Hae-Chang Rim, Jun'ichi Tsujii

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.