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Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource fordoi:10.18653/v1/d17-1024 dblp:conf/emnlp/TissierGH17 fatcat:stk4a2k4fjcmta4d3o2b4u46jm