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Leveraging Discourse Information Effectively for Authorship Attribution

7 September 2017
Su Wang
Elisa Ferracane
Raymond J. Mooney
ArXiv (abs)PDFHTML
Abstract

We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution. We present a novel method to embed discourse features in a Convolutional Neural Network text classifier, which achieves a state-of-the-art result by a substantial margin. We empirically investigate several featurization methods to understand the conditions under which discourse features contribute non-trivial performance gains, and analyze discourse embeddings.

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