Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models

  • Rikiya Takahashi
Conference paper

DOI: 10.1007/978-3-540-74958-5_36

Volume 4701 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Takahashi R. (2007) Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models. In: Kok J.N., Koronacki J., Mantaras R.L.., Matwin S., Mladenič D., Skowron A. (eds) Machine Learning: ECML 2007. ECML 2007. Lecture Notes in Computer Science, vol 4701. Springer, Berlin, Heidelberg

Abstract

Robustly estimating the state-transition probabilities of high-order Markov processes is an essential task in many applications such as natural language modeling or protein sequence modeling. We propose a novel estimation algorithm called Hierarchical Separated Dirichlet Smoothing (HSDS), where Dirichlet distributions are hierarchically assumed to be the prior distributions of the state-transition probabilities. The key idea in HSDS is to separate the parameters of a Dirichlet distribution into the precision and mean, so that the precision depends on the context while the mean is given by the lower-order distribution. HSDS is designed to outperform Kneser-Ney smoothing especially when the number of states is small, where Kneser-Ney smoothing is currently known as the state-of-the-art technique for N-gram natural language models. Our experiments in protein sequence modeling showed the superiority of HSDS both in perplexity evaluation and classification tasks.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rikiya Takahashi
    • 1
  1. 1.IBM Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato-shi, Kanagawa 242-8502Japan