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Incorporating Content Structure into Text Analysis Applications
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 fromdblp:conf/emnlp/SauperHB10 fatcat:fa62kdrbvndxblwfhhc77juyuu