Automatic Text Categorization In Terms Of Genre and Author

By Efstathios Stamatatos, N. Fakotakis, G. Kokkinakis. International Conference on Computational Logic, 2000

495 citations recorded by the source corpus.

The two main factors that characterize a text are its content and its style, and both can be used as a means of categorization. In this paper we present an approach to text categorization in terms of genre and author for Modern Greek. In contrast to previous stylometric approaches, we attempt to take full advantage of existing natural language processing (NLP) tools. To this end, we propose a set of style markers including analysis-level measures that represent the way in which the input text has been analyzed and capture useful stylistic information without additional cost. We present a set of small-scale but reasonable experiments in text genre detection, author identification, and author verification tasks and show that the proposed method performs better than the most popular distributional lexical measures, i.e., functions of vocabulary richness and frequencies of occurrence of the most frequent words. All the presented experiments are based on unrestricted text downloaded from the World Wide Web without any manual text preprocessing or text sampling. Various performance issues regarding the training set size and the significance of the proposed style markers are discussed. Our system can be used in any application that requires fast and easily adaptable text categorization in terms of stylistically homogeneous categories. Moreover, the procedure of defining analysis-level markers can be followed in order to extract useful stylistic information using existing text processing tools.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/bad78f839e2b566c4d62558827d03b9aa1c57e48

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In Artificial Intelligence

Citations in the corpus

A Quantitative Analysis of Lexical Differences Between Genders in Telephone Conversations (2005, background): “The question of gender linguistic differences shares a number of issues with stylometry and author/speaker attribution research (Stamatatos et al., 2000),(Doddington, 2001) but novel issues emerge with analysis of conversational speech, such as studying the interaction of genders.”
Computational methods in authorship attribution (2009, unknown): “…syntactic text chunkers and parsers to create features, and found that they could considerably improve results based on traditional word based analysis alone (Baayen et al. 1996; Stamatatos et al. 2000, 2001; Gamon 2004; van Halteren 2004; Chaski 2005; Uzuner and Katz 2005; Hirst & Feiguina 2007).”
A survey of modern authorship attribution methods (2009, methodology): “For example, the first sentence of this paragraph would be analyzed as following: NP[Another attempt] VP[to exploit] NP[syntactic information] VP[was proposed] PP[by Stamatatos et al. (2000)]. where NP, VP, and PP stand for noun phrase, verb phrase, and prepositional phrase, respectively.”
Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “Then, a variety of powerful statistical and machine learning algorithms can be used to build a classification model, including discriminant analysis (Chaski, 2005; Stamatatos et al., 2000; Tambouratzis et al., 2004), SVM (de Vel et al.”