Structured Prediction Problems in Natural Language Processing

author: Michael Collins, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, MIT
introducer: William Cohen, Carnegie Mellon University
published: July 24, 2008,   recorded: July 2008,   views: 3196
Categories

Slides

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.
  Delicious Bibliography

Description

Modeling language at the syntactic or semantic level is a key problem in natural language processing, and involves a challenging set of structured prediction problems. In this talk I'll describe work on machine learning approaches for syntax and semantics, with a particular focus on lexicalized grammar formalisms such as dependency grammars, tree adjoining grammars, and categorial grammars.

I'll address key issues in the following areas:
1) the design of learning algorithms for structured linguistic data;
2) the design of representations that are used within these learning algorithms;
3) the design of efficient approximate inference algorithms for lexicalized grammars, in cases where exact inference can be very expensive.

In addition, I'll describe applications to machine translation, and natural language interfaces.

See Also:

Download slides icon Download slides: icml08_collins_spp_01.pdf (635.5┬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: