Ensemble Methods for Unsupervised WSD 
 Combination methods are an effective way of improving system performance . This paper examines the benefits of system combination for unsupervised WSD . We investigate several voting- and arbiter-based combination strategies over a diverse pool of unsupervised WSD systems . Our combination methods rely on predominant senses which are derived automatically from raw text . Experiments using the SemCor and Senseval-3 data sets demonstrate that our ensembles yield significantly better results when compared with state-of-the-art.  