Abstract
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.
Abstract (translated)
经验证明，词群集可以在各种任务上提供重要的性能改进。尽管它们很重要，但它们在特征工程的标准管道中的结合更多地依赖于试错过程，其中一个评估多个超参数，例如要使用的簇的数量。为了更好地理解这些特征的作用，我们系统地评估了它们对四个任务的影响，即命名实体细分和分类以及五点情感分类和量化的任务。我们的结果强烈建议群集成员身份功能可以提高性能。
URL
https://arxiv.org/abs/1705.01265
