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

Practical Guidelines for the Selection and Evaluation of Natural Language Processing Techniques in Requirements Engineering