Unveiling the relationship between complex networks metrics and word senses
This addresses the problem of improving semantic understanding for applications like machine translation and information retrieval, though it is incremental as it builds on existing complex network methods.
The paper tackled Word Sense Disambiguation by modeling texts as complex networks, showing that local structural features like hierarchical connectivity and clustering can distinguish word senses, with the approach outperforming traditional shallow methods in half of the cases.
The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shine light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts