A complex network approach to stylometry

Abstract

Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.

PLoS ONE, 2015 (to appear)

References in corpus (9)

• Network properties of written human language
• Probing the topological properties of complex networks modeling short written texts
• Statistical keyword detection in literary corpora
• Word sense disambiguation via high order of learning in complex networks
• Authorship recognition via fluctuation analysis of network topology and word intermittency
• Identification of Literary Movements Using Complex Networks to Represent Texts
• Complex networks analysis of language complexity
• Modeling the average shortest path length in growth of word-adjacency networks
• Unveiling the relationship between complex networks metrics and word senses

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