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EMNLP
2010

Incorporating Content Structure into Text Analysis Applications

8 years 11 months ago
Incorporating Content Structure into Text Analysis Applications
In this paper, we investigate how modeling content structure can benefit text analysis applications such as extractive summarization and sentiment analysis. This follows the linguistic intuition that rich contextual information should be useful in these tasks. 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.1
Christina Sauper, Aria Haghighi, Regina Barzilay
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where EMNLP
Authors Christina Sauper, Aria Haghighi, Regina Barzilay
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