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Surface Realization Using a Featurized Syntactic Statistical Language Model Surface Realization Using a Featurized Syntactic Statistical Language Model

by Packer, Thomas L

Abstract (Summary)
An important challenge in natural language surface realization is the generation of grammatical sentences from incomplete sentence plans. Realization can be broken into a two-stage process consisting of an over-generating rule-based module followed by a ranker that outputs the most probable candidate sentence based on a statistical language model. Thus far, an n-gram language model has been evaluated in this context. More sophisticated syntactic knowledge is expected to improve such a ranker. In this thesis, a new language model based on featurized functional dependency syntax was developed and evaluated. Generation accuracies and cross-entropy for the new language model did not beat the comparison bigram language model.
Bibliographical Information:

Advisor:

School:Brigham Young University

School Location:USA - Utah

Source Type:Master's Thesis

Keywords:natural language generation processing nlp nlg bayesian networks decision trees context specific independence realization statistical model standard pipeline architecture n gram bigram syntax features machine learning

ISBN:

Date of Publication:03/06/2006

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