Authorship identification from unstructured texts
Authorship identification is a task of identifying authors of anonymous texts given examples of the writing of authors. The increasingly large volumes of anonymous texts on the Internet enhance the great yet urgent necessity for authorship identification. It has been applied to more and more practical applications including literary works, intelligence, criminal law, civil law, and computer forensics. In this paper, we propose a semantic association model about voice, word dependency relations, and non-subject stylistic words to represent the writing style of unstructured texts of various authors, design an unsupervised approach to extract stylistic features, and employ principal components analysis and linear discriminant analysis to identify authorship of texts. This paper provides a uniform quantified method to capture syntactic and semantic stylistic characteristics of and between words and phrases, and this approach can solve the problem of the independence of different dimensions to some extent. Experimental results on two English text corpora show that our approach significantly improves the overall performance over authorship identification.
authorship identification semantic association model principal components analysis linear discriminant analysis feature extraction