Unsupervised Learning for Natural Language Processing

author: Dan Klein, UC Berkeley
published: July 30, 2008,   recorded: July 2008,   views: 15417
Categories

Slides

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

Given the abundance of text data, unsupervised approaches are very appealing for natural language processing. We present three latent variable systems which achieve state-of-the-art results in domains previously dominated by fully supervised systems. For syntactic parsing, we describe a grammar induction technique which begins with coarse syntactic structures and iteratively refines them in an unsupervised fashion. The resulting coarse-to-fine grammars admit efficient coarse-to-fine inference schemes and have produced the best parsing results in a variety of languages. For co reference resolution, we describe a discourse model in which entities are shared across documents using a hierarchical Dirichlet process. In each document, entities are repeatedly rendered into mention strings by a sequential model of attentional state and anaphoric constraint. Despite being fully unsupervised, this approach is competitive with the best supervised approaches. Finally, for machine translation, we present a model which learns translation lexicons from non-parallel corpora. Alignments between word types are modeled by a prior over matchings. Given any fixed alignment, a joint density over word vectors derives from probabilistic canonical correlation analysis. This approach is capable of discovering high-precision translations, even when the underlying corpora and languages are divergent.

See Also:

Download slides icon Download slides: uai08_klein_ul.pdf (3.7 MB)

Download slides icon Download slides: uai08_klein_ul_01.ppt (15.6 MB)


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: