  This paper presents a new challenging information extraction task in the
domain of materials science. We develop an annotation scheme for marking
information on experiments related to solid oxide fuel cells in scientific
publications, such as involved materials and measurement conditions. With this
paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus
consisting of 45 open-access scholarly articles annotated by domain experts. A
corpus and an inter-annotator agreement study demonstrate the complexity of the
suggested named entity recognition and slot filling tasks as well as high
annotation quality. We also present strong neural-network based models for a
variety of tasks that can be addressed on the basis of our new data set. On all
tasks, using BERT embeddings leads to large performance gains, but with
increasing task complexity, adding a recurrent neural network on top seems
beneficial. Our models will serve as competitive baselines in future work, and
analysis of their performance highlights difficult cases when modeling the data
and suggests promising research directions.
