Aspects of Semi-Supervised and Active Learning in Conditional Random Fields

produced by: Data & Web Mining Lab
author: Nataliya Sokolovska, Macquarie University
published: Nov. 30, 2011,   recorded: September 2011,   views: 3285


Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.


Conditional random fields are among the state-of-the art approaches to structured output prediction, and the model has been adopted for various real-world problems. The supervised classification is expensive, since it is usually expensive to produce labelled data. Unlabeled data are relatively cheap, but how to use it? Unlabeled data can be used to estimate marginal probability of observations, and we exploit this idea in our work. Introduction of unlabeled data and of probability of observations into a purely discriminative model is a challenging task. We consider an extrapolation of a recently proposed semi-supervised criterion to the model of conditional random fields, and show its drawbacks. We discuss alternative usage of the marginal probability and propose a pool-based active learning approach based on quota sampling. We carry out experiments on synthetic as well as on standard natural language data sets, and we show that the proposed quota sampling active learning method is efficient.

See Also:

Download slides icon Download slides: ecmlpkdd2011_sokolovska_semisupervised_01.pdf (224.3┬áKB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: