Abstract
Many attempts have been made to apply Natural Language Processing to requirements specifications. However, typical approaches rely on shallow parsing to identify object-oriented elements of the specifications (e.g. classes, attributes, and methods). As a result, the models produced are often incomplete, imprecise, and require manual revision and validation. In contrast, we propose a deep Natural Language Understanding approach to create complete and precise formal models of requirements specifications. We combine three main elements to achieve this: (1) acquisition of lexicon from a user-supplied glossary requiring little specialised prior knowledge; (2) flexible syntactic analysis based purely on word-order; and (3) Knowledge-based Configuration unifies several semantic analysis tasks and allows the handling of ambiguities and errors. Moreover, we provide feedback to the user, allowing the refinement of specifications into a precise and unambiguous form. We demonstrate the benefits of our approach on an example from the PROMISE requirements corpus.
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Selway, M., Mayer, W., Stumptner, M. (2014). Semantic Interpretation of Requirements through Cognitive Grammar and Configuration. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_40
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DOI: https://doi.org/10.1007/978-3-319-13560-1_40
Publisher Name: Springer, Cham
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