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Published in:
2019 | OriginalPaper | Chapter
Weighting Words Using Bi-Normal Separation for Text Classification Tasks with Multiple Classes
Authors : Jean-Thomas Baillargeon, Luc Lamontagne, Étienne Marceau
Published in: Advances in Artificial Intelligence
Publisher: Springer International Publishing
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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.