Incorporating Content Structure into Text Analysis Applications
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Barzilay_Incorporating content.pdf
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Author(s)
Sauper, Christina Joan
Haghighi, Aria
Barzilay, Regina
Date Issued
October 2010
Journal
EMNLP 2010 : Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Citation
Sauper, Christina, Aria Haghighi, and Regina Barzilay. "Incorporating Content Structure into Text Analysis Applications." EMNLP 2010: Conference on Empirical Methods in Natural Language Processing, October 9-11, 2010, MIT, Massachusetts, USA.
Version
Author's final manuscript
Abstract
Information about the content structure of a
document is largely ignored by current text
analysis applications such as information extraction
and sentiment analysis. This stands
in contrast to the linguistic intuition that rich
contextual information should benefit such applications.
We present a framework which
combines a supervised text analysis application
with the induction of latent content structure.
Both of these elements are learned
jointly using the EM algorithm. The induced
content structure is learned from a large unannotated
corpus and biased by the underlying
text analysis task. We demonstrate that
exploiting content structure yields significant
improvements over approaches that rely only
on local context.
Description
URL to papers listed on conference site
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://www.lsi.upc.edu/events/emnlp2010/papers.html
