Learning representations to model the meaning of text has been a core problem in natural language understanding (NLP). The last several years have seen extensive interests on distributional approaches, in which text spans of different granularities are encoded as continuous vectors. If properly learned, such representations have been shown to help achieve the state-of-the-art performances on a variety of NLP problems.
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