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.

本研究介绍了一种新型模型，可以提取任务特定的日期和时间实体以及其否定约束条件，通过在电子邮件基础数字 AI 预约助手的任务中进行验证，与基准方法相比，在检测与会议调度相关的日期时间实体方面具有 19％的绝对增益 F - 分数，并且在检测否定约束方面比基准方法提高了 4％。