Paper: Automatic Acquisition of Language Model based on Head-Dependent Relation between Words

ACL ID P98-1119
Title Automatic Acquisition of Language Model based on Head-Dependent Relation between Words
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

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 ma- chine 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.