Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Topic segmentation via community detection in complex networks

Full text
Author(s):
de Arruda, Henrique F. [1] ; Costa, Luciano da F. [2] ; Amancio, Diego R. [1]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil
[2] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Chaos; v. 26, n. 6 JUN 2016.
Web of Science Citations: 5
Abstract

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. Published by AIP Publishing. (AU)

FAPESP's process: 14/20830-0 - Using complex networks to recognize patterns in written texts
Grantee:Diego Raphael Amancio
Support type: Regular Research Grants
FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support type: Research Projects - Thematic Grants