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.
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Notes
- 1.
Code repository: https://github.com/jtbai/extended-bns.
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Acknowledgements
The authors gratefully acknowledge the Natural Sciences and Engineering Research Council of Canada, the Chaire d’actuariat de l’Université Laval and Intact Financial Corporation for financial support, and Véronique Barras-Fugère for her illustrations.
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Baillargeon, JT., Lamontagne, L., Marceau, É. (2019). Weighting Words Using Bi-Normal Separation for Text Classification Tasks with Multiple Classes. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_41
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DOI: https://doi.org/10.1007/978-3-030-18305-9_41
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