AI News, Bayesian Analysis in Natural Language Processing

Bayesian Analysis in Natural Language Processing

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language.

One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP.

This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples.

We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area.

His research interests are in natural language processing and machine learning, with a focus on problems in structured prediction, such as syntactic and semantic parsing.

Modeling and Reasoning with Bayesian Networks

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Bayesian nonparametrics in document and language modeling

Google Tech Talks August 28, 2008 ABSTRACT Bayesian nonparametric models have garnered significant attention in recent years in both the machine learning and statistics communities....

Bayes Theorem - Natural Language Processing | University of Michigan

4 - 3 - Evaluation and Perplexity - Stanford NLP - Professor Dan Jurafsky & Chris Manning

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2 - 1 - Regular Expressions - Stanford NLP - Professor Dan Jurafsky & Chris Manning

If you are interest on more free online course info, welcome to: Professor Dan Jurafsky & Chris Manning are offering a free online course on Natural Language Processing..

Summarization Evaluation | NLP | University of Michigan

Naive Bayes Classifier - Stanford University Course