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Word clusters have been empirically shown to offer important performanceimprovements on various tasks. Despite their importance, their incorporation inthe standard pipeline of feature engineering relies more on a trial-and-errorprocedure where one evaluates several hyper-parameters, like the number ofclusters to be used. In order to better understand the role of such features wesystematically evaluate their effect on four tasks, those of named entitysegmentation and classification as well as, those of five-point sentimentclassification and quantification. Our results strongly suggest that clustermembership features improve the performance.

On the effectiveness of feature set augmentation using clusters of word embeddings