Dual-enhanced word representations based on knowledge base

In this paper, we propose an approach for enhancing word representations twice based on large-scale knowledge bases. In the first layer of enhancement, we use the knowledge base as another contextual form corresponding to the corpus and add it to the training of distributed semantics including neural network based and matrix-based. In the second layer, we utilize local features of the knowledge base to enhance the word representations by mutual reinforcement between the keyword and the strongly associated words. We evaluate our approach not only on the well-known datasets but also on a brand-new dataset, IQ-Synonym-323. The results show that our approach compares favorably to other word representations.