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Stylogenetics: Clustering-Based Stylistic Analysis of Literary Corpora

ABSTRACT Current advances in shallow parsing allow us to use results from this field in stylogenetic research, so that a new methodology for the automatic analysis of literary texts can be developed. The main pillars of this methodology - which is borrowed from topic detection research - are (i) using more complex features than the simple lexical features suggested by traditional approaches, (ii) using authors or groups of authors as a prediction class, and (iii) using clustering methods to indicate the differences and similarities between authors (i.e. stylogenetics). On the basis of the stylistic genome of authors, we try to cluster them into closely related and meaningful groups. We report on experiments with a literary corpus of five million words consisting of representative samples of female and male authors. Combinations of syntactic, token-based and lexical features constitute a profile that characterizes the style of an author. The stylogenetics methodology opens up new perspectives for literary analysis, enabling and necessitating close cooperation between literary scholars and computational linguists.

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    ABSTRACT: In this work we propose a new strategy for the authorship identification problem and we test it on an example from Romanian literature: did Radu Albala found the continuation of Mateiu Caragiale's novel "Sub pecetea tainei", or did he write himself the respective contin- uation? The proposed strategy is based on the similarity of rankings of function words; we compare the obtained results with the results obtained by a learning method (namely Support Vector Machines -SVM- with a string kernel).
    Proceedings of the International Conference on Language Resources and Evaluation, LREC 2008, 26 May - 1 June 2008, Marrakech, Morocco; 01/2008
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    ABSTRACT: We present a new corpus for computational stylometry, more specifically authorship attribution and the prediction of author personality from text. Because of the large number of authors (145), the corpus will allow previously impossible studies of variation in features considered predictive for writing style. The innovative meta-information (personality profiles of the authors) associated with these texts allows the study of personality prediction, a not yet very well researched aspect of style. In this paper, we describe the contents of the corpus and show its use in both authorship attribution and personality prediction. We focus on features that have been proven useful in the field of author recognition. Syntactic features like part-of-speech n-grams are generally accepted as not being under the author's conscious control and therefore providing good clues for predicting gender or authorship. We want to test whether these features are helpful for personality prediction and authorship attribution on a large set of authors. Both tasks are approached as text categorization tasks. First a document representation is constructed based on feature selection from the linguistically analyzed corpus (using the Memory-Based Shallow Parser (MBSP)). These are associated with each of the 145 authors or each of the four components of the Myers-Briggs Type Indicator (Introverted-Extraverted, Sensing-iNtuitive, Thinking-Feeling, Judging- Perceiving). Authorship attribution on 145 authors achieves results around 50% accuracy. Preliminary results indicate that the first two personality dimensions can be predicted fairly accurately.
    01/2008; , ISBN: 2-9517408-4-0
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    ABSTRACT: In this paper we propose a new distance function (rank distance) designed to reflect stylistic similarity between texts. To assess the ability of this distance measure to cap- ture stylistic similarity between texts, we tested it in two different machine learning settings: clustering and binary classifica- tion.
    COLING 2008, 22nd International Conference on Computational Linguistics, Posters Proceedings, 18-22 August 2008, Manchester, UK; 01/2008

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