论文标题
ONEREL：一个步骤中的一个模块的联合实体和关系提取
OneRel:Joint Entity and Relation Extraction with One Module in One Step
论文作者
论文摘要
联合实体和关系提取是自然语言处理和知识图构建中的重要任务。现有方法通常将联合提取任务分解为几个基本模块或处理步骤，以使其易于执行。然而，这种范式忽略了这样一个事实，即三重要素是相互依存和不可分割的。因此，以前的联合方法遇到了级联错误和冗余信息的问题。为了解决这些问题，在本文中，我们提出了一个新型的联合实体和关系提取模型，该模型命名为Onerel，该模型将联合提取作为细颗粒的三重分类问题。具体而言，我们的模型由基于评分的分类器和特定于关系的角标记策略组成。前者评估了令牌对和关系是否属于事实三重。后者确保了一个简单但有效的解码过程。在两个广泛使用的数据集上进行的广泛实验结果表明，所提出的方法的性能优于最先进的基线，并且在各种重叠模式和多个三元组的复杂场景上提供一致的性能增长。
Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction. Existing approaches usually decompose the joint extraction task into several basic modules or processing steps to make it easy to conduct. However, such a paradigm ignores the fact that the three elements of a triple are interdependent and indivisible. Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. Specifically, our model consists of a scoring-based classifier and a relation-specific horns tagging strategy. The former evaluates whether a token pair and a relation belong to a factual triple. The latter ensures a simple but effective decoding process. Extensive experimental results on two widely used datasets demonstrate that the proposed method performs better than the state-of-the-art baselines, and delivers consistent performance gain on complex scenarios of various overlapping patterns and multiple triples.
