Generalized Probabilistic Topic and Syntax Models for Natural Language Processing

Generalized Probabilistic Topic and Syntax Models for Natural Language Processing

This thesis proposes a generalized probabilistic approach to modelling document collections along the combined axes of both semantics and syntax. Probabilistic topic (or semantic) models view documents as random mixtures of unobserved latent topics which are themselves represented as probabilistic d...

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Bibliographic Details
Main Author: Darling, William Michael
Other Authors: Song, Fei
Language:en
Published: 2012
Subjects:
NLP
topic models
machine learning
bayesian methods
text summarization
part-of-speech tagging
syntax modelling
Online Access:http://hdl.handle.net/10214/4001

Internet

http://hdl.handle.net/10214/4001

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