论文标题
基于跨度的联合实体和关系提取增强了序列标记机制
Span-based joint entity and relation extraction augmented with sequence tagging mechanism
论文作者
论文摘要
基于跨度的联合提取同时进行文本跨度的指定实体识别（NER）和关系提取（RE）。但是，由于以前的基于跨度的模型依赖于跨度级分类，因此它们无法从令牌级标签信息中受益，这已被证明对任务有利。在本文中，我们提出了一个基于增强跨度网络（STSN）的序列标记，这是一个基于跨度的关节模型，可以利用令牌级标签信息。在STSN中，我们通过深层堆叠多个注意力层来构建核心神经架构，每个层都由三个基本注意力单元组成。一方面，核心体系结构使我们的模型能够通过序列标记机制学习令牌级标签信息，然后在基于跨度的关节提取中使用信息；另一方面，它在NER和RE之间建立了双向信息相互作用。三个基准数据集的实验结果表明，STSN在F1方面始终优于最强的基准，从而创造了新的最新结果。
Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. However, since previous span-based models rely on span-level classifications, they cannot benefit from token-level label information, which has been proven advantageous for the task. In this paper, we propose a Sequence Tagging augmented Span-based Network (STSN), a span-based joint model that can make use of token-level label information. In STSN, we construct a core neural architecture by deep stacking multiple attention layers, each of which consists of three basic attention units. On the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE. Experimental results on three benchmark datasets show that STSN consistently outperforms the strongest baselines in terms of F1, creating new state-of-the-art results.
