NLP for Knowledge Discovery and Information Extraction from Energetics Corpora

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 …

apply automated cond-mat.mtrl-sci cs.cl data discovery extraction information information extraction knowledge machine materials nlp nlp models research route systems text textual understanding unsupervised utility