论文标题
逻辑指导的语义表示学习，用于零摄影关系分类
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
论文作者
论文摘要
关系分类旨在从句子中提取实体对之间的语义关系。但是，大多数现有方法只能识别培训期间发生的可见关系类。为了在测试时间识别看不见的关系，我们探讨了零射击关系分类的问题。以前的工作将问题视为阅读理解或文本构成，这些内容必须依靠人工描述性信息来提高关系类型的可理解性。因此，对关系标签的丰富语义知识被忽略了。在本文中，我们提出了一种新颖的逻辑指导语义表示学习模型，以零射击关系分类。我们的方法通过知识图嵌入和逻辑规则通过隐式和明确的语义表示之间建立了联系和看不见的关系。广泛的实验结果表明，我们的方法可以推广到看不见的关系类型并实现有希望的改进。
Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen relations at test time, we explore the problem of zero-shot relation classification. Previous work regards the problem as reading comprehension or textual entailment, which have to rely on artificial descriptive information to improve the understandability of relation types. Thus, rich semantic knowledge of the relation labels is ignored. In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification. Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules. Extensive experimental results demonstrate that our method can generalize to unseen relation types and achieve promising improvements.
