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2015 ; 10
(8
): e0136076
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A Complex Network Approach to Stylometry
#MMPMID26313921
Amancio DR
PLoS One
2015[]; 10
(8
): e0136076
PMID26313921
show ga
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.
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