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      Authorship Attribution with Topic Models

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      Computational Linguistics

      MIT Press - Journals

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          Finding scientific topics.

          A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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            An algorithmic framework for performing collaborative filtering

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              A survey of modern authorship attribution methods

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                Author and article information

                Journal
                Computational Linguistics
                Computational Linguistics
                MIT Press - Journals
                0891-2017
                1530-9312
                June 2014
                June 2014
                : 40
                : 2
                : 269-310
                Article
                10.1162/COLI_a_00173
                © 2014

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