Relation classification is one of the most fundamental upstream tasks in natural language processing and information extraction. State-of-the-art approaches make use of various deep neural networks (DNNs) to extract higher-level features directly. They can easily access to accurate classification results by taking advantage of both local entity features and global sentential features. Recent works on relation classification devote efforts to modify these neural networks, but less attention has been paid to the feature design concerning syntax. However, from a linguistic perspective, syntactic features are essential for relation classification. In this article, we present a novel linguistically motivated approach that enhances relation classification by imposing additional syntactic constraints. We investigate to leverage syntactic skeletons along with the sentential contexts to identify hidden relation types. The syntactic skeletons are extracted under the guidance of prior syntax knowledge. During extraction, the input sentences are recursively decomposed into syntactically shorter and simpler chunks. Experimental results on the SemEval-2010 Task 8 benchmark show that incorporating syntactic skeletons into current DNN models enhances the task of relation classification. Our systems significantly surpass two strong baseline systems. One of the substantial advantages of our proposal is that this framework is extensible for most current DNN models.

中文翻译：

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利用句法骨架进行语义关系分类的可扩展框架

关系分类是自然语言处理和信息提取中最基本的上游任务之一。最先进的方法利用各种深度神经网络 (DNN) 直接提取更高级别的特征。他们可以通过利用局部实体特征和全局句子特征轻松访问准确的分类结果。最近关于关系分类的工作致力于修改这些神经网络，但很少关注与语法相关的特征设计。然而，从语言学的角度来看，句法特征对于关系分类是必不可少的。在本文中，我们提出了一种新颖的语言驱动方法，该方法通过施加额外的句法约束来增强关系分类。我们研究利用句法骨架和句子上下文来识别隐藏的关系类型。在先验句法知识的指导下提取句法骨架。在提取过程中，输入的句子被递归地分解成句法上更短更简单的块。SemEval-2010 Task 8 基准的实验结果表明，将句法骨架结合到当前的 DNN 模型中可以增强关系分类的任务。我们的系统大大超过了两个强大的基线系统。我们提议的一大优势是该框架可扩展用于大多数当前的 DNN 模型。输入的句子被递归地分解成句法上更短更简单的块。SemEval-2010 Task 8 基准的实验结果表明，将句法骨架结合到当前的 DNN 模型中可以增强关系分类的任务。我们的系统大大超过了两个强大的基线系统。我们提议的一大优势是该框架可扩展用于大多数当前的 DNN 模型。输入的句子被递归地分解成句法上更短更简单的块。SemEval-2010 Task 8 基准的实验结果表明，将句法骨架结合到当前的 DNN 模型中可以增强关系分类的任务。我们的系统大大超过了两个强大的基线系统。我们提议的一大优势是该框架可扩展用于大多数当前的 DNN 模型。