Discriminating word senses with tourist walks in complex networks
This work addresses word sense disambiguation, a key challenge in natural language processing, but is incremental as it applies a known method to a new domain.
The paper tackled the problem of discriminating senses of 10 polysemous words by applying a learning technique based on structural organization in attribute space, finding that a deterministic tourist walk characterization improved disambiguation accuracy compared to traditional complex network measurements.
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.