  We describe challenges and advantages unique to coreference resolution in the
biomedical domain, and a sieve-based architecture that leverages domain
knowledge for both entity and event coreference resolution. Domain-general
coreference resolution algorithms perform poorly on biomedical documents,
because the cues they rely on such as gender are largely absent in this domain,
and because they do not encode domain-specific knowledge such as the number and
type of participants required in chemical reactions. Moreover, it is difficult
to directly encode this knowledge into most coreference resolution algorithms
because they are not rule-based. Our rule-based architecture uses sequentially
applied hand-designed "sieves", with the output of each sieve informing and
constraining subsequent sieves. This architecture provides a 3.2% increase in
throughput to our Reach event extraction system with precision parallel to that
of the stricter system that relies solely on syntactic patterns for extraction.
