Discriminating word senses with tourist walks in complex networks*
Institute of Mathematics and Computer Science, University of São
Paulo, P.O. Box
2 Institute of Physics of São Carlos, University of São Paulo, P.O. Box 369, 13560-970 São Carlos, Brazil
Received: 11 January 2013
Received in final form: 15 April 2013
Published online: 1 July 2013
Patterns of topological arrangement are widely used for both animal and human brains in the learning process. Nevertheless, automatic learning techniques frequently overlook these patterns. In this paper, we apply a learning technique based on the structural organization of the data in the attribute space to the problem of discriminating the senses of 10 polysemous words. Using two types of characterization of meanings, namely semantical and topological approaches, we have observed significative accuracy rates in identifying the suitable meanings in both techniques. Most importantly, we have found that the characterization based on the deterministic tourist walk improves the disambiguation process when one compares with the discrimination achieved with traditional complex networks measurements such as assortativity and clustering coefficient. To our knowledge, this is the first time that such deterministic walk has been applied to such a kind of problem. Therefore, our finding suggests that the tourist walk characterization may be useful in other related applications.
Key words: Statistical and Nonlinear Physics
Supplementary material in the form of one pdf file and one zip file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2013-40025-4.
© EDP Sciences, Società Italiana di Fisica and Springer-Verlag, 2013