Log-linear Models and Conditional Random Fields
published: Nov. 19, 2008, recorded: October 2008, views: 118950
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Watch videos: (click on thumbnail to launch)
Log-linear models are a far-reaching extension of logistic regression, while con- ditional random fields (CRFs) are a special case of log-linear models suitable for so-called structured learning tasks. Structured learning means learning to predict outputs that have internal structure. For example, recognizing handwritten words is more accurate when the correlations between neighboring letters are used to reÞne predictions. This tutorial will provide a simple but thorough introduction to these new developments in machine learning that have great potential for many novel applications.
The tutorial will first explain what log-linear models are, with with concrete examples but also with mathematical generality. Next, feature-functions will be explained; these are the knowledge-representation technique underlying log-linear models. The tutorial will then present linear-chain CRFs, from the point of view that they are a special case of log-linear models. The Viterbi algorithm that makes inference tractable for linear-chain CRFs will be covered, followed by a discus- sion of inference for general CRFs. The presentation will continue with a general derivation of the gradient of log-linear models; this is the mathematical foundation of all log-linear training algorithms. Then, the tutorial will discuss two impor- tant special-case CRF training algorithms, one that is a variant of the perceptron method, and another one called contrastive divergence. Last but not least, the tu- torial will introduce publicly available software for training and using CRFs, and will explain a practical application of CRFs with hands-on detail.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !