Surface Realization Using a Featurized Syntactic Statistical Language Model

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.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Packer, Thomas L.

• 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
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/384

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1383