AeroBERT-NER: Named-Entity Recognition for Aerospace Requirements Engineering using BERT

Abstract

Organizations predominantly use natural language (NL) for requirements elicitation, development, and management since NL is easy to understand and use by stakeholders with varying levels of experience, unlike specialized model-based languages, which require training to use. However, NL requirements entail ambiguities associated with language, a tedious and error- prone manual examination process, difficulties verifying requirement completeness, and failure to recognize and use technical terms effectively. Modern complex systems warrant an integrated and holistic approach to their development to capture their numerous interrelationships. This need has spurred a trend toward model-centric approaches to engineering as compared to traditional document-based methods. Transitioning to Model-Based Systems Engineering (MBSE) requires standardized, machine-readable requirements, but the conversion of NL requirements into models is hindered by the ambiguities and inconsistencies inherent in NL. The field of Natural Language Processing (NLP) has shown promise in facilitating the conversion of NL requirements into a format that enables their standardization and use in a model-based environment. Named-Entities Recognition (NER), in particular, can be leveraged to help address the ambiguities in NL requirements. To be of value, however, a language model needs to be fine-tuned to recognize aerospace-specific terms. To that end, we developed an annotated aerospace corpus and fine-tuned the BERT language model on the corpus to create aeroBERT-NER: a new model for identifying named entities (NEs) in aerospace requirements. A comparison between aeroBERT-NER and BERTBASE-NER showed the improved performance of aeroBERT-NER in identifying NEs within a set of aerospace requirements. The NEs identified by the model were used to create a glossary to facilitate consistent use of technical terms and language in aerospace requirements.