Improving Supervised Sense Disambiguation with Web-Scale Selectors

This paper introduces a method to improve supervised word sense disambiguation performance by including a new class of features which leverage contextual information from large unannotated corpora. This new feature class, selectors, contains words that appear in other corpora with the same local context as a given lexical instance. We show that support vector sense classifiers trained with selectors achieve higher accuracy than those trained only with standard features, producing error reductions of 15.4% and 6.9% on standard coarse-grained and fine-grained disambiguation tasks respectively. Furthermore, we find an error reduction of 9.3% when including selectors for the classification step of named-entity recognition over a representative sample of OntoNotes. These significant improvements come free of any human annotation cost, only requiring unlabeled Web-Scale corpora.