Word Sense Induction: Triplet-Based Clustering and Automatic Evaluation

In this paper a novel solution to automatic and unsupervised word sense induction (WSI) is introduced. It represents an instantiation of the ‘one sense per collocation’ observation (Gale et al., 1992). Like most existing approaches it utilizes clustering of word co-occurrences. This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs. The combination with a two-step clustering process using sentence co-occurrences as features allows for accurate results. Additionally, a novel and likewise automatic and unsupervised evaluation method inspired by Schütze’s (1992) idea of evaluation of word sense disambiguation algorithms is employed. Offering advantages like reproducability and independency of a given biased gold standard it also enables automatic parameter optimization of the WSI algorithm.