Natural Language Processing Applied on Large Scale Data Extraction from Scientific Papers in Fuel Cells

Abstract

Natural language processing (NLP) has a great potential to help scientists automatically extract information from large-scale text datasets. In this paper, we focus on the process of NLP — including text acquisition, text preprocessing, word embedding training, and named entity recognition — applied on 106,181 abstracts of fuel cell papers. Then we evaluate our trained model on its ability of analogy, use the model to analyze the research trend in fuel cell materials and predict new materials. To the best of our knowledge, it is the first time that NLP has been applied in the field of fuel cells. This data-driven technique is demonstrated to have the potential to promote the discoveries of new fuel cell materials.