A survey of noise reduction methods for distant supervision

Benjamin Roth, Tassilo Barth, Michael Wiegand, D. Klakow
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引用次数: 57

Abstract

We survey recent approaches to noise reduction in distant supervision learning for relation extraction. We group them according to the principles they are based on: at-least-one constraints, topic-based models, or pattern correlations. Besides describing them, we illustrate the fundamental differences and attempt to give an outlook to potentially fruitful further research. In addition, we identify related work in sentiment analysis which could profit from approaches to noise reduction.
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远程监理降噪方法综述
我们综述了最近在远程监督学习中用于关系提取的降噪方法。我们根据它们所基于的原则对它们进行分组:至少一个约束、基于主题的模型或模式相关性。除了描述它们之外,我们还说明了基本差异,并试图对可能富有成果的进一步研究进行展望。此外,我们确定了情感分析中的相关工作,这些工作可以从降噪方法中获益。
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