Balancing between over-weighting and under-weighting in supervised term weighting

Supervised term weighting could improve the performance of text categorization. A way proven to be effective is to assign larger weight to terms with more imbalanced distributions across categories. This paper shows that supervised term weighting should not just assign large weights to imbalanced terms, but should also control the trade-off between over-weighting and under-weighting. Over-weighting, a new concept proposed in this paper, is caused by the improper handling of singular terms and too large ratios between term weights. To prevent over-weighting, we present three regularization techniques: add-one smoothing, sublinear scaling and bias term. Add-one smoothing is used to handle singular terms. Sublinear scaling and bias term shrink the ratios between term weights. However, if sublinear functions scale down term weights too much, or the bias term is too large, under-weighting would occur and harm the performance. It is therefore critical to balance between over-weighting and under-weighting. Inspired by this insight, we also propose a new supervised term weighting scheme, regularized entropy (re). Our re employs entropy to measure term distribution, and introduces the bias term to control over-weighting and under-weighting. Empirical evaluations on topical and sentiment classification datasets indicate that sublinear scaling and bias term greatly influence the performance of supervised term weighting, and our re enjoys the best results in comparison with existing schemes.