  State of the art research for date-time entity extraction from text is task
agnostic. Consequently, while the methods proposed in literature perform well
for generic date-time extraction from texts, they don't fare as well on task
specific date-time entity extraction where only a subset of the date-time
entities present in the text are pertinent to solving the task. Furthermore,
some tasks require identifying negation constraints associated with the
date-time entities to correctly reason over time. We showcase a novel model for
extracting task-specific date-time entities along with their negation
constraints. We show the efficacy of our method on the task of date-time
understanding in the context of scheduling meetings for an email-based digital
AI scheduling assistant. Our method achieves an absolute gain of 19\% f-score
points compared to baseline methods in detecting the date-time entities
relevant to scheduling meetings and a 4\% improvement over baseline methods for
detecting negation constraints over date-time entities.
