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Supervised Understanding of Word Embeddings
[article]
2020
arXiv
pre-print
Pre-trained word embeddings are widely used for transfer learning in natural language processing. The embeddings are continuous and distributed representations of the words that preserve their similarities in compact Euclidean spaces. However, the dimensions of these spaces do not provide any clear interpretation. In this study, we have obtained supervised projections in the form of the linear keyword-level classifiers on word embeddings. We have shown that the method creates interpretable
arXiv:2006.13299v1
fatcat:2qvoug75wvelpccifqslv6l7o4