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Unsupervised learning of rhetorical structure with un-topic models

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In this paper we investigate whether unsupervised models can be used to induce conventional aspects of rhetorical language in scientific writing. We rely on the intuition that the rhetorical language used in a document is general in nature and independent of the document’s topic. We describe a Bayesian latent-variable model that implements this intuition. In two empirical evaluations based on the task of argumentative zoning (AZ), we demonstrate that our generality hypothesis is crucial for distinguishing between rhetorical and topical language and that features provided by our unsupervised model trained on a large corpus can improve the performance of a supervised AZ classifier.

This talk is part of the NLIP Seminar Series series.

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