Are Linear Models Right for Language?

author: Fernando C. N. Pereira, Google, Inc.
recorded by: Center for Language and Speech Processing
published: Feb. 15, 2012,   recorded: November 2008,   views: 3236
Categories

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.
  Bibliography

Description

Over the last decade, linear models have become the standard machine learning approach for supervised classification, ranking, and structured prediction natural language processing. They can handle very high-dimensional problem representations, they are easy to set up and use, and they extend naturally to complex structured problems. But there is something unsatisfying in this work. The geometric intuitions behind linear models were developed with low-dimensional, continuous problems, while natural language problems involve very high dimension, discrete representations with long tailed distributions. Do the orignal intuitions carry over? In particular, do standard regularization methods make any sense for language problems? I will give recent experimental evidence that there is much to do in making linear model learning more suited to the statistics of language.

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: