Incorporating Content Structure into Text Analysis Applications

Christina Sauper, Aria Haghighi, Regina Barzilay
2010 Conference on Empirical Methods in Natural Language Processing  
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
more » ... 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
dblp:conf/emnlp/SauperHB10 fatcat:fa62kdrbvndxblwfhhc77juyuu