关系抽取中远监督错误标注消除

目前远监督方法被广泛应用于关系抽取任务。然而,远监督方法中存在大量错误标注现象,给远监督方法的学习效果带来了很大的影响。提出利用语义Jaccard度量关系短语与依存词间语义相似性的错误标注消除方法。消除错误标注后的训练数据用于训练模型,完成关系抽取。实验结果表明:该方法可以有效消除错误标注,提高关系抽取的性能。

Reducing wrong labels in distant supervision for relation extraction

Distant supervision has been widely used for relation extraction recently. In the distant supervision, many labels may to wrongly marked, which exerts a bad impact on relation extraction. A method to reduce wrong labels was introduced by using the semantic Jaccard to measure semantic similarity between the relation phrases and the dependency terms. The training data after reducing wrong labels was used to train the relation extractors. The experimental results show that the proposed method can effectively reduce wrong labels and improve the relation extraction performance compared with the state-of-art methods.