论文标题
关系提取与上下文关系嵌入（CRE）
Relation Extraction with Contextualized Relation Embedding (CRE)
论文作者
论文摘要
关系提取是在给定的两个实体之间识别关系实例的任务，而知识基础建模是代表知识基础的任务，就实体之间的关系而言。本文提出了一种关系提取任务的架构，将语义信息与知识库建模以新颖的方式集成在一起。现有的关系提取方法要么不利用知识库建模，要么使用经过单独训练的KB模型来进行重新任务。我们提出了一个模型体系结构，该模型将KB建模内化为关系提取。该模型将一种新颖的方法应用于上下文化的关系嵌入中，然后将其与参数化实体嵌入一起用于分数关系实例。拟议的CRE模型可以在源自《纽约时报》注释的语料库和Freebase的数据集上实现最先进的性能。源代码已提供。
Relation extraction is the task of identifying relation instance between two entities given a corpus whereas Knowledge base modeling is the task of representing a knowledge base, in terms of relations between entities. This paper proposes an architecture for the relation extraction task that integrates semantic information with knowledge base modeling in a novel manner. Existing approaches for relation extraction either do not utilize knowledge base modelling or use separately trained KB models for the RE task. We present a model architecture that internalizes KB modeling in relation extraction. This model applies a novel approach to encode sentences into contextualized relation embeddings, which can then be used together with parameterized entity embeddings to score relation instances. The proposed CRE model achieves state of the art performance on datasets derived from The New York Times Annotated Corpus and FreeBase. The source code has been made available.
