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Word embedding, a process to automatically learn the mathematical representations of words from unlabeled text corpora, has gained a lot of attention recently. Since words are the basic units of a natural language, the more precisely we can represent the morphological, syntactic and semantic properties of words, the better we can support downstream Natural Language Processing (NLP) tasks. Since traditional word embeddings are mainly designed to capture the semantic relatedness betweendoi:10.24963/ijcai.2020/676 dblp:conf/ijcai/GaoCR0020 fatcat:n3hj4lad2vcphpmzdnwgflp7x4