  Natural Language Processing (NLP) is now a cornerstone of requirements
automation. One compelling factor behind the growing adoption of NLP in
Requirements Engineering (RE) is the prevalent use of natural language (NL) for
specifying requirements in industry. NLP techniques are commonly used for
automatically classifying requirements, extracting important information, e.g.,
domain models and glossary terms, and performing quality assurance tasks, such
as ambiguity handling and completeness checking. With so many different NLP
solution strategies available and the possibility of applying machine learning
alongside, it can be challenging to choose the right strategy for a specific RE
task and to evaluate the resulting solution in an empirically rigorous manner.
In this chapter, we present guidelines for the selection of NLP techniques as
well as for their evaluation in the context of RE. In particular, we discuss
how to choose among different strategies such as traditional NLP, feature-based
machine learning, and language-model-based methods. Our ultimate hope for this
chapter is to serve as a stepping stone, assisting newcomers to NLP4RE in
quickly initiating themselves into the NLP technologies most pertinent to the
RE field.
