Unsupervised cue-words discovery for tag-sense disambiguation: comparing dissimilarity metrics

Although tagging simplifies resource browsing and retrieval, it suffers from several issues: among them are redundancy and ambiguity. In this work we focus on the problem of resolving tag word-sense ambiguity within a typical semi-automatic tagging procedure. In that process a user proposes a tag for a resource, if the tag is found to be related to more than one context, she is provided with two or more cues among which to choose, so as to remove the tag ambiguity. Key phases, in such a disambiguation procedure, are ambiguous tag detection and cue discovery. Both should rely on effective word-to-context relatedness metrics. Among the most effective relatedness metrics are those defined on the basis of a feature vector representation of the words. In this work we compare different word-to-context relatedness metrics in terms of effectiveness within the disambiguation process. We propose to use a metrics derived from a Maximum Likelihood estimator of the Jensen-Shannon Divergence among feature-count histograms and we show that such a metrics performs -- in terms of quality of the output -- better than both the Jensen-Shannon and the Symmetrized Kullback-Leibler divergence between histograms. We study the relative gain in quality within the task of unsupervised cue discovery by using a synthetic language corpus.