Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy
Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowdsource annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to iteratively and interactively correcting mislabeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable amount of time.
52nd Annual Meeting of the Association for Computational Linguistics, 2014.
© Association for Computational Linguistics, 2014
Martineau, J., Chen, L., Cheng, D., & Sheth, A. P. (2014). Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.