Topic segmentation via community detection in complex networks

Abstract

Many real systems have been modelled 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 findings, 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 representation fails in capturing other textual features, such as the organization in topics or subjects. In this context, 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 favours 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 have 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 semantical features of texts.

The dynamics of knowledge acquisition via self-learning in complex networks

“…As future works, we intend to conduct a systematic study of the influence of topology, dynamics and start- ing node in a large variety of complex systems, including social [24], information [25][26][27], semantical [28,29] and technological networks [30]. We also intend to study whether some of the adopted models can reproduce the dynamics of acquisition and transmission of knowledge.…”

Multilayer networks for text analysis with multiple data types

“…The novelty of our multilayer approach, in comparison to other approaches using multilayer networks, is the inclusion of the text layer. The importance of using a bipartite multigraph layer [22] to represent the text, instead of alternative "word networks" [14,29,30], is that it contains the complete information of word occurrence in documents and allows for a formal connection to topic-modelling methods [15,20]. We now investigate the properties of the multilayer network described above, based on known results in networks and textual data.…”

Term-Community-Based Topic Detection with Variable Resolution

“…al. [32] employ modularity maximization [33], using the Louvain algorithm [34]; similar approaches can be found in [35][36][37][38]. Louvainbased community detection was also applied in [39,40], in combination with a principle component analysis, to co-word maps.…”