Unsupervised Decomposition of a Multi-Author Document Based on Naive-Bayesian Model

Publication Type:
Conference Proceeding
Citation:
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers), 2015, 2 pp. 501 - 505
Issue Date:
2015-08-01
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This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the authors, except the number of the authors of that document. The key idea is to exploit the difference in the posterior probability of the Naive-Bayesian model to increase the precision of the clustering assignment and the accuracy of the classification process of our method. Experimental results show that the proposed method outperforms two state-of-the-art methods. © 2015 Association for Computational Linguistics.
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