Self-organizing Markov models and their application to part-of-speech tagging
Jin-Dong Kim, Hae‐Chang Rim, Jun'ich Tsujii · 2003
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.