Automatic acquistion of language model based on head-dependent relation between words

Authors:
Seungmi Lee;Key-Sun Choi
Affiliations:
Center for Artificial Intelligence Research, Korea Advanced Institute of Science and Technology;Center for Artificial Intelligence Research, Korea Advanced Institute of Science and Technology
Venue:
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Year:
1998

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Abstract

Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statistical machine translation. In this paper, we present a language modeling based on a kind of simple dependency grammar. The grammar consists of head-dependent relations between words and can be learned automatically from a raw corpus using the reestimation algorithm which is also introduced in this paper. Our experiments show that the proposed model performs better than n-gram models at 11% to 11.5% reductions in test corpus entropy.