Development of a Language Model for Named-Entity-Recognition in Aerospace Requirements

Abstract

We address the challenges inherent in converting natural language (NL) requirements into machine-readable formats by investigating the application of named-entity recognition (NER) within the aerospace domain. Recognizing the necessity for domain-specific language models, we developed an open-source annotated aerospace corpus and fine-tuned different versions of the BERT language model on the corpus to create aeroBERT-NER: a new model for identifying named entities (NEs) in the aerospace domain. A comparison between aeroBERT-NER and BERTBASE-NER demonstrated the superior performance of aeroBERT-NER in identifying NEs within a set of aerospace requirements. The identified NEs contribute to the development of a glossary, promoting consistent terminology usage in aerospace requirements and addressing challenges associated with the standardization of NL requirements.