NLP for Knowledge Discovery and Information Extraction from Energetics Corpora

The study of energetics necessarily involves numerous scientific domains, spanning shock physics and detonation science, fluid dynamics, material science, thermodynamics, and chemical synthesis. The plethora of sub-disciplines of math, physics, chemistry, and engineering pose a challenge to practitioners who would wish to amass an expertise of energetics. Furthermore, maintaining awareness of advancements in energetics research is complicated by the exponential rate at which new research is published across scientific disciplines, including energetics. Thus, the development of automated and intelligent approaches for extracting knowledge from papers, reports, textbooks, and patents related to energetics could aid researchers and accelerate progress in energetics science. Natural Language Processing (NLP) is a sub-field of linguistics, computer science, and Machine Learning (ML) involving the interactions between computers and human (natural) languages. NLP techniques are used to analyze and generate human language, allowing computers to read, interpret, and understand text and speech. In the context of energetics research, NLP can be used to analyze large volumes of textual data, such as scientific papers, technical reports, and patents, in order to extract relevant information about the concepts that underlie and explain energetics phenomenon. Furthermore, NLP can enable natural language understanding that could be further applied to text mining journal articles and performing numerous natural language tasks such as classification, summarization, and recommendation. Overall, the use of NLP in energetics research has the potential to enhance our understanding of energetic materials and phenomenon, and assist in the development novel propellants, explosives, and pyrotechnics.