In the context of requirements engineering, relation extraction involves identifying and documenting the associations between different requirements artefacts. When dealing with textual requirements (i.e., requirements expressed using natural language), relation extraction becomes a cognitively challenging task, especially in terms of ambiguity and required effort from domain-experts. Hence, in highly-adaptive, large-scale environments, effective and efficient automated relation extraction using natural language processing techniques becomes essential. In this chapter, we present a comprehensive overview of natural language-based relation extraction from text-based requirements. We initially describe the fundamentals of requirements relations based on the most relevant literature in the field, including the most common requirements relations types. The core of the chapter is composed by two main sections: (i) natural language techniques for the identification and categorization of equirements relations (i.e., syntactic vs. semantic techniques), and (ii) information extraction methods for the task of relation extraction (i.e., retrieval-based vs. machine learning-based methods). We complement this analysis with the state-of-the-art challenges and the envisioned future research directions. Overall, this chapter aims at providing a clear perspective on the theoretical and practical fundamentals in the field of natural language-based relation extraction.

翻译：在需求工程领域，关系抽取涉及识别并记录不同需求制品之间的关联。当处理文本化需求（即使用自然语言表述的需求）时，关系抽取成为一项认知挑战性任务，尤其在歧义性和领域专家所需投入精力方面表现突出。因此，在高度自适应的大规模环境中，利用自然语言处理技术实现高效自动化的关系抽取变得至关重要。本章对基于自然语言的文本需求关系抽取进行全面综述。我们首先依据该领域最具相关性的文献阐述需求关系的基本原理，包括最常见的需求关系类型。本章核心由两个主要部分组成：（i）用于需求关系识别与分类的自然语言技术（即句法技术与语义技术），以及（ii）面向关系抽取任务的信息抽取方法（即基于检索的方法与基于机器学习的方法）。我们结合当前最前沿的挑战与未来研究方向的展望来补充此项分析。总体而言，本章旨在为基于自然语言的关系抽取领域提供清晰的理论与实践基础视角。