  Computational synthesis planning approaches have achieved recent success in
organic chemistry, where tabulated synthesis procedures are readily available
for supervised learning. The syntheses of inorganic materials, however, exist
primarily as natural language narratives contained within scientific journal
articles. This synthesis information must first be extracted from the text in
order to enable analogous synthesis planning methods for inorganic materials.
In this work, we present a system for automatically extracting structured
representations of synthesis procedures from the texts of materials science
journal articles that describe explicit, experimental syntheses of inorganic
compounds. We define the structured representation as a set of linked events
made up of extracted scientific entities and evaluate two unsupervised
approaches for extracting these structures on expert-annotated articles: a
strong heuristic baseline and a generative model of procedural text. We also
evaluate a variety of supervised models for extracting scientific entities. Our
results provide insight into the nature of the data and directions for further
work in this exciting new area of research.
