论文标题
使用深度学习方法的材料称为实体识别（MNER）知识驱动材料
Material Named Entity Recognition (MNER) for Knowledge-driven Materials Using Deep Learning Approach
论文作者
论文摘要
科学文献在材料科学领域中包含丰富的尖端知识，以及有用的数据（例如，来自实验结果，材料特性和结构的数值数据）。这些数据对于数据驱动的机器学习（ML）和深度学习（DL）方法至关重要，以加速材料发现。由于出版物数量越来越大，人类很难手动检索和保留这些知识。在这种情况下，我们研究了基于BI-LSTM的深神网络模型，以从已发表的科学文章中检索知识。所提出的基于神经网络的深度网络模型的F-1得分对于名称实体识别（MNER）任务的F-1分数为\ 〜97 \％。该研究涉及动机，相关工作，方法论，超参数和整体绩效评估。该分析提供了对实验结果的见解，并指出了当前研究的未来方向。
The scientific literature contains a wealth of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical data from experimental results, material properties and structure). These data are critical for data-driven machine learning (ML) and deep learning (DL) methods to accelerate material discovery. Due to the large and growing number of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. The proposed deep neural network-based model achieves an f-1 score of \~97\% for the Material Named Entity Recognition (MNER) task. The study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. The analysis provides insight into the results of the experiment and points to future directions for current research.
