This document contains a discussion of the F1 score evaluation used in the
article 'Relation Classification with Entity Type Restriction' by Shengfei Lyu,
Huanhuan Chen published on Findings of the Association for Computational
Linguistics: ACL-IJCNLP 2021. The authors created a system named RECENT and
claim it achieves (then) a new state-of-the-art result 75.2 (previous 74.8) on
the TACRED dataset, while after correcting errors and reevaluation the final
result is 65.16

讨论了 Shengfei Lyu 和 Huanhuan Chen 在 ACL-IJCNLP 2021 上发表的文章 “受实体类型限制的关系分类” 中使用的 F1 分数评估，讨论了他们创建的 RECENT 系统在 TACRED 数据集上的表现，最终结果为 65.16。

关于评估最新关系抽取系统 F1 分数的研究

About Evaluation of F1 Score for RECENT Relation Extraction System

Relation classification aims to predict a relation between two entities in a
sentence. The existing methods regard all relations as the candidate relations
for the two entities in a sentence. These methods neglect the restrictions on
candidate relations by entity types, which leads to some inappropriate
relations being candidate relations. In this paper, we propose a novel
paradigm, RElation Classification with ENtity Type restriction (RECENT), which
exploits entity types to restrict candidate relations. Specially, the mutual
restrictions of relations and entity types are formalized and introduced into
relation classification. Besides, the proposed paradigm, RECENT, is
model-agnostic. Based on two representative models GCN and SpanBERT
respectively, RECENT_GCN and RECENT_SpanBERT are trained in RECENT.
Experimental results on a standard dataset indicate that RECENT improves the
performance of GCN and SpanBERT by 6.9 and 4.4 F1 points, respectively.
Especially, RECENT_SpanBERT achieves a new state-of-the-art on TACRED.

本文中，我们提出了一个新的模型架构，称为 RECENT，它利用实体类型限制候选关系，并形式化介绍了关系和实体类型之间的相互约束关系。在 GCN 和 SpanBERT 这两个表现较好的模型上进行实验，实验结果表明，RECENT 可以分别提升它们 6.9 个和 4.4 个 F1 点，并且 RECENT_SpanBERT 在 TACRED 上取得了新的最佳效果。