@inproceedings{azab-etal-2019-towards,
title = "Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines",
author = "Azab, Mahmoud and
Dadian, Stephane and
Nastase, Vivi and
An, Larry and
Mihalcea, Rada",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1122",
doi = "10.18653/v1/D19-1122",
pages = "1255--1260",
abstract = "We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain. We describe the data collection process and the annotations performed by medical professionals, including illness and personal attributes (name, age, gender, family relationships) for the patient and their family members. An initial system that performs argument identification and relation extraction shows promising results {--} average F-score of 0.87 on complex sentences on the targeted relations.",
}
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%0 Conference Proceedings
%T Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines
%A Azab, Mahmoud
%A Dadian, Stephane
%A Nastase, Vivi
%A An, Larry
%A Mihalcea, Rada
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F azab-etal-2019-towards
%X We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain. We describe the data collection process and the annotations performed by medical professionals, including illness and personal attributes (name, age, gender, family relationships) for the patient and their family members. An initial system that performs argument identification and relation extraction shows promising results – average F-score of 0.87 on complex sentences on the targeted relations.
%R 10.18653/v1/D19-1122
%U https://aclanthology.org/D19-1122
%U https://doi.org/10.18653/v1/D19-1122
%P 1255-1260
Markdown (Informal)
[Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines](https://aclanthology.org/D19-1122) (Azab et al., EMNLP-IJCNLP 2019)
ACL