  Word clusters have been empirically shown to offer important performance
improvements on various tasks. Despite their importance, their incorporation in
the standard pipeline of feature engineering relies more on a trial-and-error
procedure where one evaluates several hyper-parameters, like the number of
clusters to be used. In order to better understand the role of such features we
systematically evaluate their effect on four tasks, those of named entity
segmentation and classification as well as, those of five-point sentiment
classification and quantification. Our results strongly suggest that cluster
membership features improve the performance.
