NLP for Knowledge Discovery and Information Extraction from Energetics
  Corpora
Francis G. VanGessel Efrem Perry Salil Mohan Oliver M. Barham Mark Cavolowsky
We present a demonstration of the utility of NLP for aiding research into
energetic materials and associated systems. The NLP method enables machine
understanding of textual data, offering an automated route to knowledge
discovery and information extraction from energetics text. We apply three
established unsupervised NLP models: Latent Dirichlet Allocation, Word2Vec, and
the Transformer to a large curated dataset of energetics-related scientific
articles. We demonstrate that each NLP algorithm is capable of identifying
energetic topics and concepts, generating a language model which aligns with
Subject Matter Expert knowledge. Furthermore, we present a document
classification pipeline for energetics text. Our classification pipeline
achieves 59-76\% accuracy depending on the NLP model used, with the highest
performing Transformer model rivaling inter-annotator agreement metrics. The
NLP approaches studied in this work can identify concepts germane to energetics
and therefore hold promise as a tool for accelerating energetics research
efforts and energetics material development.