Sequence Learning from Data with Multiple Labels
Mark H. Dredze, Partha Talukdar, Koby Crammer · 2010
Abstract. We present novel algorithms for learning structured predictors from instances with multiple labels in the presence of noise. The proposed algorithms improve performance on two standard NLP tasks when we have a small amount of training data (low quantity) and when the labels are noisy (low quality). In these settings, the methods improve performance over using a single label, in some cases exceeding performance using gold labels. Our methods could be used in a semi-supervised setting, where a limited amount of labeled data could be combined with a rule based automatic labeling of unlabeled data with multiple possible labels. 1