  The high volume of published chemical patents and the importance of a timely
acquisition of their information gives rise to automating information
extraction from chemical patents. Anaphora resolution is an important component
of comprehensive information extraction, and is critical for extracting
reactions. In chemical patents, there are five anaphoric relations of interest:
co-reference, transformed, reaction associated, work up, and contained. Our
goal is to investigate how the performance of anaphora resolution models for
reaction texts in chemical patents differs in a noise-free and noisy
environment and to what extent we can improve the robustness against noise of
the model.
