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

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.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Khaled Aldebei, Xiangjian He, Jie Yang 0002
• Keywords: ACL 2015, Computational Linguistics