Class-based variable memory length markov model

Abstract

In this paper, we present a class-based variable memory length Markov model and its learning algorithm. This is an extension of a variable memory length Markov model. Our model is based on a class-based probabilistic suffix tree, whose nodes have an automatically acquired word-class relation. We experimentally compared our new model with a word-based bi-gram model, a word-based tri-gram model, a class-based bi-gram model, and a word-based variable memory length Markov model. The results show that a class-based variable memory length Markov model outperforms the other models in perplexity and model size.