@inproceedings{uppunda-etal-2021-adapting,
title = "Adapting Coreference Resolution for Processing Violent Death Narratives",
author = "Uppunda, Ankith and
Cochran, Susan and
Foster, Jacob and
Arseniev-Koehler, Alina and
Mays, Vickie and
Chang, Kai-Wei",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.361",
doi = "10.18653/v1/2021.naacl-main.361",
pages = "4553--4559",
abstract = "Coreference resolution is an important compo-nent in analyzing narrative text from admin-istrative data (e.g., clinical or police sources).However, existing coreference models trainedon general language corpora suffer from poortransferability due to domain gaps, especiallywhen they are applied to gender-inclusive datawith lesbian, gay, bisexual, and transgender(LGBT) individuals. In this paper, we an-alyzed the challenges of coreference resolu-tion in an exemplary form of administrativetext written in English: violent death nar-ratives from the USA{'}s Centers for DiseaseControl{'}s (CDC) National Violent Death Re-porting System. We developed a set of dataaugmentation rules to improve model perfor-mance using a probabilistic data programmingframework. Experiments on narratives froman administrative database, as well as existinggender-inclusive coreference datasets, demon-strate the effectiveness of data augmentationin training coreference models that can betterhandle text data about LGBT individuals.",
}
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<abstract>Coreference resolution is an important compo-nent in analyzing narrative text from admin-istrative data (e.g., clinical or police sources).However, existing coreference models trainedon general language corpora suffer from poortransferability due to domain gaps, especiallywhen they are applied to gender-inclusive datawith lesbian, gay, bisexual, and transgender(LGBT) individuals. In this paper, we an-alyzed the challenges of coreference resolu-tion in an exemplary form of administrativetext written in English: violent death nar-ratives from the USA’s Centers for DiseaseControl’s (CDC) National Violent Death Re-porting System. We developed a set of dataaugmentation rules to improve model perfor-mance using a probabilistic data programmingframework. Experiments on narratives froman administrative database, as well as existinggender-inclusive coreference datasets, demon-strate the effectiveness of data augmentationin training coreference models that can betterhandle text data about LGBT individuals.</abstract>
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%0 Conference Proceedings
%T Adapting Coreference Resolution for Processing Violent Death Narratives
%A Uppunda, Ankith
%A Cochran, Susan
%A Foster, Jacob
%A Arseniev-Koehler, Alina
%A Mays, Vickie
%A Chang, Kai-Wei
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F uppunda-etal-2021-adapting
%X Coreference resolution is an important compo-nent in analyzing narrative text from admin-istrative data (e.g., clinical or police sources).However, existing coreference models trainedon general language corpora suffer from poortransferability due to domain gaps, especiallywhen they are applied to gender-inclusive datawith lesbian, gay, bisexual, and transgender(LGBT) individuals. In this paper, we an-alyzed the challenges of coreference resolu-tion in an exemplary form of administrativetext written in English: violent death nar-ratives from the USA’s Centers for DiseaseControl’s (CDC) National Violent Death Re-porting System. We developed a set of dataaugmentation rules to improve model perfor-mance using a probabilistic data programmingframework. Experiments on narratives froman administrative database, as well as existinggender-inclusive coreference datasets, demon-strate the effectiveness of data augmentationin training coreference models that can betterhandle text data about LGBT individuals.
%R 10.18653/v1/2021.naacl-main.361
%U https://aclanthology.org/2021.naacl-main.361
%U https://doi.org/10.18653/v1/2021.naacl-main.361
%P 4553-4559
Markdown (Informal)
[Adapting Coreference Resolution for Processing Violent Death Narratives](https://aclanthology.org/2021.naacl-main.361) (Uppunda et al., NAACL 2021)
ACL
- Ankith Uppunda, Susan Cochran, Jacob Foster, Alina Arseniev-Koehler, Vickie Mays, and Kai-Wei Chang. 2021. Adapting Coreference Resolution for Processing Violent Death Narratives. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4553–4559, Online. Association for Computational Linguistics.