Using Unlabeled Data to Improve Author Identification

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Provided by: International Association of Computer Science & Information Technology (IACSIT)
Topic: Big Data
Format: PDF
Authorship attribution may be considered as a text categorization problem. Text categorization requires a large number of training examples which are particularly difficult to obtain in the case of authorship attribution task. In this paper, the authors investigate the possibility of using Web-based text-mining methods for the identification of the author of a given poem. In particular, they propose a semi-supervised method that is specially suited to work with just few training examples in order to tackle the problem of the lack of data with the same writing style.
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