@inproceedings{patra-etal-2020-schedule,
title = "To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints",
author = "Patra, Barun and
Fufa, Chala and
Bhattacharya, Pamela and
Lee, Charles",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.678",
doi = "10.18653/v1/2020.emnlp-main.678",
pages = "8445--8455",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints
%A Patra, Barun
%A Fufa, Chala
%A Bhattacharya, Pamela
%A Lee, Charles
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F patra-etal-2020-schedule
%X 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.
%R 10.18653/v1/2020.emnlp-main.678
%U https://aclanthology.org/2020.emnlp-main.678
%U https://doi.org/10.18653/v1/2020.emnlp-main.678
%P 8445-8455
Markdown (Informal)
[To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints](https://aclanthology.org/2020.emnlp-main.678) (Patra et al., EMNLP 2020)
ACL