Topic segmentation via community detection in complex networks

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.