Automatic Text Categorization in Terms of Genre and Author

Abstract

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.

Music Performer Verification Based on Learning Ensembles

“…These measures have been defined in and applied to areas of similar characteristics, such as speaker verification [4] and author verification [9] and are defined as follows:…”

Large Scale Unstructured Document Classification Using Unlabeled Data and Syntactic Information

“…Because the current NLP techniques do not provide accurate information enough to be used in information retrieval, text chunking is considered to be an alternative to full parsing [14]. Text chunking is to divide text into syntactically related non-overlapping segments of words.…”

“…Stamatatos et al showed experimentally that the syntactic information among various kinds of linguistic information is a reliable clue for document classification [14]. One additional benefit in using syntactic information for document classification by the co-training algorithm is that it is somewhat independent from term weights.…”

“…In addition, unfortunately, the current NLP (natural language processing) techniques are not able to provide accurate results in syntax analyzing. However, some studies show that text chunks can give enough information on syntax analysis instead of full parsing [14].…”

Tensor Space Models for Authorship Identification

“…Beyond the traditional approach based on human experts, this procedure can be automated by computational tools able to capture and match the stylistic properties of texts and authors [26,32,2]. The main idea is that by measuring some textual features we can distinguish between texts written by different authors.…”