As the amount and variety of energetics research increases, machine aware
topic identification is necessary to streamline future research pipelines. The
makeup of an automatic topic identification process consists of creating
document representations and performing classification. However, the
implementation of these processes on energetics research imposes new
challenges. Energetics datasets contain many scientific terms that are
necessary to understand the context of a document but may require more complex
document representations. Secondly, the predictions from classification must be
understandable and trusted by the chemists within the pipeline. In this work,
we study the trade-off between prediction accuracy and interpretability by
implementing three document embedding methods that vary in computational
complexity. With our accuracy results, we also introduce local interpretability
model-agnostic explanations (LIME) of each prediction to provide a localized
understanding of each prediction and to validate classifier decisions with our
team of energetics experts. This study was carried out on a novel labeled
energetics dataset created and validated by our team of energetics experts.

通过三种文档嵌入方法的实现，研究了预测准确性和可解释性之间的权衡，通过提供本地可解释性模型无关解释，为管道内的化学家提供预测的本地化理解和验证分类器决策。