Song Wei, Gu Weishuai, Zhu Fuxin, Park Soon Cheol
IEEE Trans Neural Netw Learn Syst. 2024 Jul;35(7):9523-9537. doi: 10.1109/TNNLS.2023.3233971. Epub 2024 Jul 8.
Distantly supervised relation extraction (DSRE) aims to identify semantic relations from massive plain texts. A broad range of the prior research has leveraged a series of selective attention mechanisms over sentences in a bag to extract relation features without considering dependencies among the relation features. As a result, potential discriminative information existed in the dependencies is ignored, causing a decline in the performance of extracting entity relations. In this article, we focus on going beyond the selective attention mechanisms and propose a new framework termed interaction-and-response network (IR-Net) that adaptively recalibrates the features of sentence, bag, and group levels by explicitly modeling interdependencies among the features on each level. The IR-Net consists of a series of interactive and responsive modules throughout feature hierarchy, seeking to strengthen its power of learning salient discriminative features for distinguishing entity relations. We conduct extensive experiments on three benchmark DSRE datasets, including NYT-10, NYT-16, and Wiki-20m. The experimental results demonstrate that the IR-Net brings obvious improvements in performance when comparing ten state-of-the-art DSRE methods for entity relation extraction.
远距离监督关系抽取（DSRE）旨在从海量纯文本中识别语义关系。此前的大量研究利用了一系列针对句子包的选择性注意力机制来提取关系特征，而未考虑关系特征之间的依赖性。结果，依赖性中存在的潜在判别信息被忽略，导致实体关系抽取性能下降。在本文中，我们致力于超越选择性注意力机制，提出一种新的框架，称为交互与响应网络（IR-Net），该框架通过显式建模每个层次特征之间的相互依赖性，自适应地重新校准句子、句子包和组层次的特征。IR-Net由贯穿特征层次结构的一系列交互式和响应式模块组成，旨在增强其学习显著判别特征以区分实体关系的能力。我们在三个基准DSRE数据集上进行了广泛实验，包括NYT-10、NYT-16和Wiki-20m。实验结果表明，与十种用于实体关系抽取的最新DSRE方法相比，IR-Net在性能上带来了明显提升。
