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Survey of Neural Text Representation Models
2020
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In natural language processing, text needs to be transformed into a machine-readable representation before any processing. The quality of further natural language processing tasks greatly depends on the quality of those representations. In this survey, we systematize and analyze 50 neural models from the last decade. The models described are grouped by the architecture of neural networks as shallow, recurrent, recursive, convolutional, and attention models. Furthermore, we categorize these
doi:10.3390/info11110511
fatcat:veamykmme5cm5jhsllyc4xl7ma