Open-world Relation Extraction (OpenRE) has recently garnered significant
attention. However, existing approaches tend to oversimplify the problem by
assuming that all unlabeled texts belong to novel classes, thereby limiting the
practicality of these methods. We argue that the OpenRE setting should be more
aligned with the characteristics of real-world data. Specifically, we propose
two key improvements: (a) unlabeled data should encompass known and novel
classes, including hard-negative instances; and (b) the set of novel classes
should represent long-tail relation types. Furthermore, we observe that popular
relations such as titles and locations can often be implicitly inferred through
specific patterns, while long-tail relations tend to be explicitly expressed in
sentences. Motivated by these insights, we present a novel method called KNoRD
(Known and Novel Relation Discovery), which effectively classifies explicitly
and implicitly expressed relations from known and novel classes within
unlabeled data. Experimental evaluations on several Open-world RE benchmarks
demonstrate that KNoRD consistently outperforms other existing methods,
achieving significant performance gains.

这篇论文提出了一种名为 KNoRD 的方法，该方法在未标记数据中识别明确和隐含的关系，并有效地分类已知和新的关系类别，是 Open-world Relation Extraction 领域的最新研究成果。

现实世界中基于半监督的开放式通用关系发现

Open-world Semi-supervised Generalized Relation Discovery Aligned in a  Real-world Setting

Relation extraction (RE) has achieved remarkable progress with the help of
pre-trained language models. However, existing RE models are usually incapable
of handling two situations: implicit expressions and long-tail relation types,
caused by language complexity and data sparsity. In this paper, we introduce a
simple enhancement of RE using $k$ nearest neighbors ($k$NN-RE). $k$NN-RE
allows the model to consult training relations at test time through a
nearest-neighbor search and provides a simple yet effective means to tackle the
two issues above. Additionally, we observe that $k$NN-RE serves as an effective
way to leverage distant supervision (DS) data for RE. Experimental results show
that the proposed $k$NN-RE achieves state-of-the-art performances on a variety
of supervised RE datasets, i.e., ACE05, SciERC, and Wiki80, along with
outperforming the best model to date on the i2b2 and Wiki80 datasets in the
setting of allowing using DS. Our code and models are available at:
this https URL.

介绍一种使用 $k$ 最近邻进行增强的关系提取方法 $k$NN-RE，可以在测试时间通过最近邻搜索查阅训练关系，并提供了一种简单却有效的方法来解决由于语言复杂性和数据稀疏性引起的隐式表达和长尾关系类型的两个问题，并且观察到 $k$NN-RE 是利用远程监督（DS）数据进行 RE 的有效方法。在 ACE05、SciERC 和 Wiki80 等多种受控制的 RE 数据集上，提出的 $k$NN-RE 取得了最高水平的性能，并在允许使用 DS 的情况下在 i2b2 和 Wiki80 数据集上优于迄今为止最好的模型。