复杂网络中游客步行的词义判别
Discriminating word senses with tourist walks in complex networks
摘要：
拓扑排列模式广泛应用于动物和人类大脑的学习过程。然而，自动学习技术经常忽略这些模式。本文将一种基于属性空间数据结构化组织的学习技术应用于10个多义词的义项识别问题。使用两种类型的表征的含义，即语义和拓扑的方法，我们已经观察到显着的准确率在这两种技术中确定合适的含义。最重要的是，我们已经发现，基于确定性的游客步行的表征，提高了消歧过程中相比，与传统的复杂网络测量，如重复性和聚类系数实现的歧视。据我们所知，这是第一次，这样的确定性行走已被应用到这样一种问题。因此，我们的研究结果表明，游客步行表征可能是有用的其他相关应用。
英文摘要：
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.
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