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This paper presents a new challenging information extraction task in thedomain of materials science. We develop an annotation scheme for markinginformation on experiments related to solid oxide fuel cells in scientificpublications, such as involved materials and measurement conditions. With thispaper, we publish our annotation guidelines, as well as our SOFC-Exp corpusconsisting of 45 open-access scholarly articles annotated by domain experts. Acorpus and an inter-annotator agreement study demonstrate the complexity of thesuggested named entity recognition and slot filling tasks as well as highannotation quality. We also present strong neural-network based models for avariety of tasks that can be addressed on the basis of our new data set. On alltasks, using BERT embeddings leads to large performance gains, but withincreasing task complexity, adding a recurrent neural network on top seemsbeneficial. Our models will serve as competitive baselines in future work, andanalysis of their performance highlights difficult cases when modeling the dataand suggests promising research directions.

The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain