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        <title>PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features</title>
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        <namePart type="given">Matias</namePart>
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    <name type="personal">
        <namePart type="given">Mauricio</namePart>
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    <name type="personal">
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            <namePart type="given">Davy</namePart>
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    <abstract>This paper describes our approaches used to solve the SocialDisNER task, which belongs to the Social Media Mining for Health Applications (SMM4H) shared task. This task aims to identify disease mentions in tweets written in Spanish. The proposed model is an architecture based on the FLERT approach. It consists of fine-tuning a language model that creates an input representation of a sentence based on its neighboring sentences, thus obtaining the document-level context. The best result was obtained using an ensemble of six language models using the FLERT approach. The system achieved an F1 score of 0.862, significantly surpassing the average performance among competitor models of 0.680 on the test partition.</abstract>
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%0 Conference Proceedings
%T PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features
%A Rojas, Matias
%A Barros, Jose
%A Martin, Kinan
%A Araneda-Hernandez, Mauricio
%A Dunstan, Jocelyn
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F rojas-etal-2022-pln
%X This paper describes our approaches used to solve the SocialDisNER task, which belongs to the Social Media Mining for Health Applications (SMM4H) shared task. This task aims to identify disease mentions in tweets written in Spanish. The proposed model is an architecture based on the FLERT approach. It consists of fine-tuning a language model that creates an input representation of a sentence based on its neighboring sentences, thus obtaining the document-level context. The best result was obtained using an ensemble of six language models using the FLERT approach. The system achieved an F1 score of 0.862, significantly surpassing the average performance among competitor models of 0.680 on the test partition.
%U https://aclanthology.org/2022.smm4h-1.15
%P 52-54

Markdown (Informal)

[PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features](https://aclanthology.org/2022.smm4h-1.15) (Rojas et al., SMM4H 2022)

• PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features (Rojas et al., SMM4H 2022)

ACL

• Matias Rojas, Jose Barros, Kinan Martin, Mauricio Araneda-Hernandez, and Jocelyn Dunstan. 2022. PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 52–54, Gyeongju, Republic of Korea. Association for Computational Linguistics.