Quantifying Context Overlap for Training Word Embeddings

Most models for learning word embeddings are trained based on the context information of words, more precisely first order cooccurrence relations. In this paper, a metric is designed to estimate second order cooccurrence relations based on context overlap. The estimated values are further used as the augmented data to enhance the learning of word embeddings by joint training with existing neural word embedding models. Experimental results show that better word vectors can be obtained for word similarity tasks and some downstream NLP tasks by the enhanced approach.