  In the context of requirements engineering, relation extraction is the task
of documenting the traceability between 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
requirements 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.
