MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in
  the Materials Science Domain
Timo Pierre Schrader Matteo Finco Stefan Grünewald Felix Hildebrand Annemarie Friedrich
Keeping track of all relevant recent publications and experimental results
for a research area is a challenging task. Prior work has demonstrated the
efficacy of information extraction models in various scientific areas.
Recently, several datasets have been released for the yet understudied
materials science domain. However, these datasets focus on sub-problems such as
parsing synthesis procedures or on sub-domains, e.g., solid oxide fuel cells.
In this resource paper, we present MuLMS, a new dataset of 50 open-access
articles, spanning seven sub-domains of materials science. The corpus has been
annotated by domain experts with several layers ranging from named entities
over relations to frame structures. We present competitive neural models for
all tasks and demonstrate that multi-task training with existing related
resources leads to benefits.