Sequence Learning from Data with Multiple Labels
Sequence Learning from Data with Multiple Labels
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发表时间：
2009
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通讯作者：
Mark Dredze;P. Talukdar;K. Crammer
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作者：
Mark Dredze;P. Talukdar;K. Crammer
我们提出了新的算法，用于学习结构化预测的实例中存在噪声的多个标签。当我们有少量的训练数据（低数量）和标签有噪声（低质量）时，所提出的算法提高了两个标准NLP任务的性能。在这些设置中，这些方法比使用单个标签提高了性能，在某些情况下超过了使用金标签的性能。我们的方法可以在半监督设置中使用，其中有限数量的标记数据可以与基于规则的具有多个可能标签的未标记数据的自动标记相结合。
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.