论文标题
重新审查无监督的关系提取
Revisiting Unsupervised Relation Extraction
论文作者
论文摘要
无监督的关系提取（URE）从原始文本中提取命名实体之间的关系，而无需手动标记的数据和现有知识库（KBS）。可以将URE方法分类为生成和判别方法，这些方法依赖于手工制作的特征或表面形式。但是，我们证明，通过仅使用指定的实体诱导关系类型，我们可以在两个流行的数据集上胜过现有的方法。我们与其他URE技术对我们的发现进行比较和评估，以确定URE中的重要特征。我们得出的结论是，实体类型为URE提供了强烈的感应偏见。
Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstrate that by using only named entities to induce relation types, we can outperform existing methods on two popular datasets. We conduct a comparison and evaluation of our findings with other URE techniques, to ascertain the important features in URE. We conclude that entity types provide a strong inductive bias for URE.
