Using a symbolic language parser to Improve Markov language models

Abstract

This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model.