Abstract

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.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2006-03-13

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd1195

Keywords

natural language generation, natural language processing, NLP, NLG, Bayesian networks, decision trees, context specific independence, realization, statistical language model, standard pipeline architecture, n-gram (bigram) language model, syntax, features, statistical model, machine learning

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