Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning
Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning
复制标题
文本洞察：通过深度学习加速有机材料知识提取
DOI：
10.1002/pra2.497
复制
发表时间：
2021-09
影响因子：
--
通讯作者：
Xintong Zhao;Steven Lopez;S. Saikin;Xiaohua Hu;Jane Greenberg
中科院分区：
文献类型：
--
作者：
Xintong Zhao;Steven Lopez;S. Saikin;Xiaohua Hu;Jane Greenberg
科学文献是知识共享的重要资源之一。研究人员将科学文献作为设计实验的第一步。鉴于文献数量庞大且不断增长，阅读和手动提取知识的常见方法过于耗时，从而在研究周期中造成瓶颈。这一挑战几乎涉及所有科学领域。对于材料科学来说，分布在数百万出版物中的实验数据对于预测材料性能和设计新材料非常有帮助。然而，直到最近，研究人员才探索了主要用于无机材料的知识提取的计算方法。本研究旨在探讨有机材料的知识提取。我们建立了一个研究数据集，由来自92，667篇摘要的855个注释和708，376个未注释句子组成。我们使用命名实体识别（NER）和BiLSTM-CNN-CRF深度学习模型来自动从文献中提取关键知识。早期阶段的结果显示了自动化知识提取的巨大潜力。本文介绍了我们的研究结果和监督的知识提取，可以适应其他科学领域的框架。
Scientific literature is one of the most significant resources for sharing knowledge. Researchers turn to scientific literature as a first step in designing an experiment. Given the extensive and growing volume of literature, the common approach of reading and manually extracting knowledge is too time consuming, creating a bottleneck in the research cycle. This challenge spans nearly every scientific domain. For the materials science, experimental data distributed across millions of publications are extremely helpful for predicting materials properties and the design of novel materials. However, only recently researchers have explored computational approaches for knowledge extraction primarily for inorganic materials. This study aims to explore knowledge extraction for organic materials. We built a research dataset composed of 855 annotated and 708,376 unannotated sentences drawn from 92,667 abstracts. We used named‐entity‐recognition (NER) with BiLSTM‐CNN‐CRF deep learning model to automatically extract key knowledge from literature. Early‐phase results show a high potential for automated knowledge extraction. The paper presents our findings and a framework for supervised knowledge extraction that can be adapted to other scientific domains.