Paper: Self-Organizing Markov Models And Their Application To Part-Of-Speech Tagging

ACL ID P03-1038
Title Self-Organizing Markov Models And Their Application To Part-Of-Speech Tagging
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

This paper presents a method to de- velop a class of variable memory Markov models that have higher memory capac- ity than traditional (uniform memory) Markov models. The structure of the vari- able memory models is induced from a manually annotated corpus through a de- cision tree learning algorithm. A series of comparative experiments show the result- ing models outperform uniform memory Markov models in a part-of-speech tag- ging task.