Location of Repository

Rational Interpolation Of Maximum Likelihood Predictors In Stochastic Language Modeling

By Ernst Günter Schukat-Talamazzini, Florian Gallwitz, Stefan Harbeck and Volker Warnke

Abstract

In our paper, we address the problem of estimating stochastic language models based on n-gram statistics. We present a novel approach, rational interpolation, for the combination of a competing set of conditional n-gram word probability predictors, which consistently outperforms the traditional linear interpolation scheme. The superiority of rational interpolation is substantiated by experimental results from language modeling, speech recognition, dialog act classification, and language identification. 1. INTRODUCTION In our paper, we address the problem of estimating stochastic language models P (w) for sentences w = w1 : : : wT of words w t from a finite vocabulary V. The joint distribution P (w) can be decomposed by the wellknown chain rule P (w) = T Y t=1 P (w t jw t\Gamma1 1 ) = T Y t=1 P (w t j w1 : : : w t\Gamma1 ) (1) into a product of conditional word probabilities (by w t s we denote the substring ws : : : w t of w). The latter, in turn, are usually approximate..

Year: 1997
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.6018
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www5.informatik.uni-erl... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.