  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.
