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.

应用自然语言处理方法，利用三种无监督模型 (Latent Dirichlet Allocation、Word2Vec 和 Transformer) 分析大规模特定主题科学文章的能源相关内容并实现文本的信息提取和知识发现，同时展示了能源材料研究中潜在的主题和概念，并开发了文档分类方法以加速能源研究和材料开发工作。

能源语料库中的知识发现和信息抽取的自然语言处理

NLP for Knowledge Discovery and Information Extraction from Energetics  Corpora

Most unsupervised NLP models represent each word with a single point or
single region in semantic space, while the existing multi-sense word embeddings
cannot represent longer word sequences like phrases or sentences. We propose a
novel embedding method for a text sequence (a phrase or a sentence) where each
sequence is represented by a distinct set of multi-mode codebook embeddings to
capture different semantic facets of its meaning. The codebook embeddings can
be viewed as the cluster centers which summarize the distribution of possibly
co-occurring words in a pre-trained word embedding space. We introduce an
end-to-end trainable neural model that directly predicts the set of cluster
centers from the input text sequence during test time. Our experiments show
that the per-sentence codebook embeddings significantly improve the
performances in unsupervised sentence similarity and extractive summarization
benchmarks. In phrase similarity experiments, we discover that the multi-facet
embeddings provide an interpretable semantic representation but do not
outperform the single-facet baseline.

提出一种新的文本编码方法，使用多模编码来表示文本序列的不同语义因素，并从输入文本序列中直接预测聚类中心来改善无监督句子相似性和提取式摘要。