@inproceedings{bagherzadeh-bergler-2020-clac,
title = "{CL}a{C} at {SMM}4{H} 2020: Birth Defect Mention Detection",
author = "Bagherzadeh, Parsa and
Bergler, Sabine",
booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.smm4h-1.32",
pages = "168--170",
abstract = "For the detection of personal tweets, where a parent speaks of a child{'}s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture. To address the task, we compile several gazetteer lists from resources such as MeSH and GI. The proposed system obtains .69 for μF1 score in the SMM4H 2020 Task 5 where the competition average is .65.",
}
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<abstract>For the detection of personal tweets, where a parent speaks of a child’s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture. To address the task, we compile several gazetteer lists from resources such as MeSH and GI. The proposed system obtains .69 for μF1 score in the SMM4H 2020 Task 5 where the competition average is .65.</abstract>
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%0 Conference Proceedings
%T CLaC at SMM4H 2020: Birth Defect Mention Detection
%A Bagherzadeh, Parsa
%A Bergler, Sabine
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F bagherzadeh-bergler-2020-clac
%X For the detection of personal tweets, where a parent speaks of a child’s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture. To address the task, we compile several gazetteer lists from resources such as MeSH and GI. The proposed system obtains .69 for μF1 score in the SMM4H 2020 Task 5 where the competition average is .65.
%U https://aclanthology.org/2020.smm4h-1.32
%P 168-170
Markdown (Informal)
[CLaC at SMM4H 2020: Birth Defect Mention Detection](https://aclanthology.org/2020.smm4h-1.32) (Bagherzadeh & Bergler, SMM4H 2020)
ACL
- Parsa Bagherzadeh and Sabine Bergler. 2020. CLaC at SMM4H 2020: Birth Defect Mention Detection. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 168–170, Barcelona, Spain (Online). Association for Computational Linguistics.