# Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora

arXiv: https://arxiv.org/abs/1903.00415 · HTML: https://mlsanity.com/paper/1903.00415/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance & Efficiency on a Specific Domain](https://mlsanity.com/paper/2412.00532/) (2024)
- [Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora](https://mlsanity.com/paper/2206.00773/) (2022)
- [NLP for Knowledge Discovery and Information Extraction from Energetics Corpora](https://mlsanity.com/paper/2402.06964/) (2024)

## Abstract (arXiv)

The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge. In this work we explore how techniques from natural language processing and machine learning can be used to automatically extract chemical insights from large collections of documents. We first describe how to download and process documents from a variety of sources - journal articles, conference proceedings (including NTREM), the US Patent & Trademark Office, and the Defense Technical Information Center archive on archive.org. We present a custom NLP pipeline which uses open source NLP tools to identify the names of chemical compounds and relates them to function words ("underwater", "rocket", "pyrotechnic") and property words ("elastomer", "non-toxic"). After explaining how word embeddings work we compare the utility of two popular word embeddings - word2vec and GloVe. Chemical-chemical and chemical-application relationships are obtained by doing computations with word vectors. We show that word embeddings capture latent information about energetic materials, so that related materials appear close together in the word embedding space.