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Conditional Random Fields : Theory and Application

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If you have a question about this talk, please contact Shakir Mohamed.

Conditional Random Fields are a probabilistic framework for labelling and segmentation of structured data, such as sequences. The core principal of such models is to define a conditional probability distribution over label sequences as opposed to a joint distribution over label and observation sequences (which is the approach taken using Hidden Markov Models). This approach is able to circumvent many of the issues typically encountered when using HMMs and other sequential models. In most applications across a diverse list of fields, CRFs have been found to outperform classical sequential models.

This talk aims to serve as an introduction to CRF models, while covering some of the details of the implementation of such models and discussing their application. A theoretical motivation for such models will firstly be presented. Thereafter, specific topics relating to design, training and implementation of CRFs will be discussed.

The following resources may be useful:

J. Lafferty, A. McCallum and F. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, In Proc. 18th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA (2001) 282–289

R. Klinger and K. Tomanek, Classical Probabilistic Models and Conditional Random Fields, Algorithm Engineering Report TR07 -2-013, Department of Computer Science, Dortmund University of Technology, December 2007. ISSN 1864 -4503

C. Sutton and A. McCallum, An Introduction to Conditional Random Fields for Relational Learning, In Introduction to Statistical Relational Learning, Edited by Lise Getoor and Ben Taskar. MIT Press

This talk is part of the Machine Learning Reading Group @ CUED series.

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