论文标题
通过利用知识图链接预测来改善关系提取
Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction
论文作者
论文摘要
关系提取（RE）旨在预测句子中主题和对象之间的关系，而知识图链接预测（KGLP）旨在预测一组对象，o，给定一个主体和与知识图的关系。 These two problems are closely related as their respective objectives are intertwined: given a sentence containing a subject and an object o, a RE model predicts a relation that can then be used by a KGLP model together with the subject, to predict a set of objects O. Thus, we expect object o to be in set O. In this paper, we leverage this insight by proposing a multi-task learning approach that improves the performance of RE models by jointly training on RE and KGLP任务。我们通过将其应用于现有的几种RE模型，并在经验上说明它如何帮助他们实现一致的性能提高来说明我们的方法的普遍性。
Relation extraction (RE) aims to predict a relation between a subject and an object in a sentence, while knowledge graph link prediction (KGLP) aims to predict a set of objects, O, given a subject and a relation from a knowledge graph. These two problems are closely related as their respective objectives are intertwined: given a sentence containing a subject and an object o, a RE model predicts a relation that can then be used by a KGLP model together with the subject, to predict a set of objects O. Thus, we expect object o to be in set O. In this paper, we leverage this insight by proposing a multi-task learning approach that improves the performance of RE models by jointly training on RE and KGLP tasks. We illustrate the generality of our approach by applying it on several existing RE models and empirically demonstrate how it helps them achieve consistent performance gains.
