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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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Available from: Kim Luyckx, Aug 11, 2015
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    • "Compared with other machine learning and statistical approaches , clustering was relatively rarely used in stylistic investigations. However, few researchers (Holmes et al., 2001; Labbé and Labbé, 2006; Luyckx et al., 2006) have recently proved that clustering can be a useful tool in computational stylistic studies. An agglomerative hierarchical clustering algorithm (Duda et al., 2001) arranges a set of objects in a family tree (dendogram ) according to their similarity. "
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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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    • "Compared with other machine learning and statistical approaches, clustering was relatively rarely used in stylistic investigations. However, few researchers (Labbé and Labbé, 2001; Luyckx et al., 2006) have recently proved that clustering can be a useful tool in computational stylistic studies. "
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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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