Abstract
Representing words, the basic units in language, is one of the most fundamental concerns in Information Retrieval, Natural Language Processing (NLP), and related fields. In this paper, we reviewed most of the approaches of word representation in vector space (especially state-of-the-art word embedding) and their related downstream applications. The limitations, trends and their connection to traditional vector space based approaches are also discussed.
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