Wednesday, February 2, 2011

Comments for February 2, 2011

To recap, I read

Coreference Resolution in a Modular, Entity-Centered Model

In the paper, the authors attempt to apply a entity-centered model that shares many features with the one used in the focus paper. Their approach is unsupervised, and their generative model makes use of distributional entity types, which is one of the major factors that separates this paper from the template-filling approach.

The flow of the generative model consists of 3 basic modules, one for semantics, one for discourse, and one for mention generation. From what I understand it feels that these respective components in the focus paper are almost exactly the same, except roles are replaced by types. Intuitively speaking, template-filling and co-reference resolution seem like sibling problems, which only further supports the fact that the two papers share authors and year of publication.

The learning procedure divides the variables into subgroups and does optimizations in a round-robin update scheme. Again, this is not unlike the variational EM algorithm used in the focus paper. The form of evaluation is on several standard co-reference resolution metrics that I am mostly unfamiliar with, but overall, the results show sizable improvement over previous work in almost all metrics (reduced error rates).

What's cool about this work is that it exploits information at multiple levels, considering both individual entities and entity types. Of course, the motivation for this is that a better grasp on semantic constraints is the key to improved co-reference revolution systems.

Still getting used to reading research papers...


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