Learning word embeddings via context grouping

Recently, neural-network based word embedding models have been shown to produce high-quality distributional representations capturing both semantic and syntactic information. In this paper, we propose a grouping-based context predictive model by considering the interactions of context words, which generalizes the widely used CBOW model and Skip-Gram model. In particular, the words within a context window are split into several groups with a grouping function, where words in the same group are combined while different groups are treated as independent. To determine the grouping function, we propose a relatedness hypothesis stating the relationship among context words and propose several context grouping methods. Experimental results demonstrate better representations can be learned with suitable context groups.