State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in the literature perform well for generic date-time extraction from texts, they don\u2019t 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in the literature perform well for generic date-time extraction from texts, they don\u2019t 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 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