Abstract
Transforming a sentence into a two-dimensional (2D) representation (e.g., the table filling) has the ability to unfold a semantic plane, where an element of the plane is a word-pair representation of a sentence which may denote a possible relation representation composed of two named entities. The 2D representation is effective in resolving overlapped relation instances. However, in related works, the representation is directly transformed from a raw input. It is weak to utilize prior knowledge, which is important to support the relation extraction task. In this paper, we propose a two-dimensional feature engineering method in the 2D sentence representation for relation extraction. Our proposed method is evaluated on three public datasets (ACE05 Chinese, ACE05 English, and SanWen) and achieves the state-of-the-art performance. The results indicate that two-dimensional feature engineering can take advantage of a two-dimensional sentence representation and make full use of prior knowledge in traditional feature engineering. Our code is publicly available at this https URL
Abstract (translated)
将句子转换为二维（2D）表示（例如，表格填充）具有展开语义平面的能力，其中平面上一个元素是句子词对表示，这可能表示由两个命名实体组成的可能关系表示。2D表示在解决重叠关系实例方面非常有效。然而，在相关工作中，该表示是从原始输入直接转换的。它对利用先验知识较弱，这对于支持关系提取任务非常重要。在本文中，我们在关系提取的二维句子表示中提出了一种二维特征工程方法。我们对三个公开数据集（ACE05中文，ACE05英文和SanWen）进行了评估，并实现了最先进的性能。结果表明，二维特征工程可以利用二维句子表示充分利用传统特征工程中的先验知识。我们的代码公开可用，请点击以下链接:
URL
https://arxiv.org/abs/2404.04959
