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Application of Bayesian Framework in Natural Language Understanding

Author(s): Goyal Pawan | Behera Laxmidhar | McGinnity T

Journal: IETE Technical Review
ISSN 0256-4602

Volume: 25;
Issue: 5;
Start page: 251;
Date: 2008;
Original page

Keywords: Bayesian networks | Information retrieval | Natural language understanding | Word sense disambiguation.

A natural language understanding (NLU) system has to handle a large amount of data. A graphical model serves as an advantageous tool for data analysis encoding the dependencies among variables and learning causal relationships. Over the last two decades, the Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. It is an ideal representation for combining prior knowledge; it avoids overfitting of data. Efficient algorithms have been developed for learning Bayesian networks from data, allowing Bayesian networks to be applied to a wide category of problems. In this paper, we give a comprehensive and state-of the-art introduction to the application of Bayesian networks in different aspects of an NLU system, with emphasis on information retrieval. The extensions and variants of Bayesian networks applied to NLU problems have been described. Examples of application examples are given, in order to illustrate the use of Bayesian networks.
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