Evaluating Unsupervised Ensembles when applied to Word Sense Induction

Ensembles combine knowledge from distinct machine learning approaches into a general flexible system. While supervised ensembles frequently show great benefit, unsupervised ensembles prove to be more challenging. We propose evaluating various unsupervised ensembles when applied to the unsupervised task of Word Sense Induction with a framework for combining diverse feature spaces and clustering algorithms. We evaluate our system using standard shared tasks and also introduce new automated semantic evaluations and supervised baselines, both of which highlight the current limitations of existing Word Sense Induction evaluations.