论文标题
关系提取和分类的生成模型
A Generative Model for Relation Extraction and Classification
论文作者
论文摘要
关系提取（RE）是一项重要的信息提取任务，它为许多NLP应用程序（例如知识库人群和问题答案）提供了必不可少的信息。在本文中，我们提出了一种用于关系提取和分类的新颖生成模型（我们称为GREC），其中RE被建模为序列到序列生成任务。我们探索了源和目标序列的各种编码表示形式，并设计有效的方案，使GREC能够在三个基准RE数据集上实现最新性能。此外，我们引入了负抽样和解码缩放技术，该技术提供了一种灵活的工具来调整模型的精确性和回忆性能。我们的方法可以扩展以在一通句中从句子中提取所有关系三元。尽管单通道的方法会造成一定的性能损失，但在计算上效率更高。
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering. In this paper, we present a novel generative model for relation extraction and classification (which we call GREC), where RE is modeled as a sequence-to-sequence generation task. We explore various encoding representations for the source and target sequences, and design effective schemes that enable GREC to achieve state-of-the-art performance on three benchmark RE datasets. In addition, we introduce negative sampling and decoding scaling techniques which provide a flexible tool to tune the precision and recall performance of the model. Our approach can be extended to extract all relation triples from a sentence in one pass. Although the one-pass approach incurs certain performance loss, it is much more computationally efficient.
