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- W2945027673 abstract "An important usage of natural language processing is creating vector representations of documents as features in a classification task. The traditional bag-of-word approach uses one-hot vector representations of words that aggregate into sparse vector document representation. This representation can be enhanced by weighting words that contribute the most to a classification task. In this paper, we propose a generalization of the Bi-Normal Separation metric that enhances vector representations of documents and outperforms TF-IDF scaling algorithms for one-of-m classification tasks." @default.
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- W2945027673 date "2019-01-01" @default.
- W2945027673 modified "2023-10-13" @default.
- W2945027673 title "Weighting Words Using Bi-Normal Separation for Text Classification Tasks with Multiple Classes" @default.
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- W2945027673 doi "https://doi.org/10.1007/978-3-030-18305-9_41" @default.
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