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.
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