Identification and Interpretation of NSWs Using Variational Bayesian Inference in Bengali News Corpus
Chandan Kundu
International Journal of Computational Linguistics Research, Vol. 5, No. 4, 2014.
DOI: 
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Abstract:
The Bayesian model for prediction problems requires setting up the prior/hyper prior structures that go through the process of integration. However, these formulated integrals are not tractable analytically. Moreover, application of Markov Chain Monte Carlo (MCMC) methods to solve these integralsare slow in nature, especially if the parameter space
is high dimensional. The key idea behind the Bayesian inference is to marginalize over unknown parameters, rather than make point estimation. This technique avoids severe over-fitting problems and allows direct model comparison.
In this paper, we presented a model using variational Bayesian inference for identification and interpretation of Non Standard Words in Bengali news corpus. The variational methods extend the practicality of Bayesian inference to complex Bayesian models and â€œmedium sizedâ€ data sets. The Variational Bayesian inference aims to approximate posterior distribution by a simpler distribution for which marginalization is tractable.