Token-Level Multilingual Epidemic Dataset for Event Extraction

2021
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Authors: Stephen Mutuvi; Emanuela Boros; Antoine Doucet; Gaël Lejeune; Adam Jatowt; Moses Odeo
Published in: Linking Theory and Practice of Digital Libraries
Publisher: Springer International Publishing

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Abstract

The chapter delves into the limitations of conventional disease surveillance methods and the potential of data-driven approaches using internet-based data sources. It introduces a token-level multilingual dataset adapted from the DAnIEL corpus for epidemic event extraction, focusing on disease names and locations. The study compares the performance of various neural sequence labeling architectures and pre-trained language models, such as BERT and XLM-RoBERTa, against a specialized baseline system. The results indicate that pre-trained language models outperform traditional models, offering promising avenues for enhancing epidemic surveillance. The chapter concludes with recommendations for further research, including a more fine-grained analysis of the methods and the classification of relevant and irrelevant documents.

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Notes

This work has been supported by the European Union’s Horizon 2020 research and innovation program under grants 770299 (NewsEye) and 825153 (Embeddia). It has also been supported by the French Embassy in Kenya and the French Foreign Ministry.

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Title: Token-Level Multilingual Epidemic Dataset for Event Extraction
Authors: Stephen Mutuvi
Emanuela Boros
Antoine Doucet
Gaël Lejeune
Adam Jatowt
Moses Odeo
Publisher: Springer International Publishing
Book: Linking Theory and Practice of Digital Libraries
Print ISBN: 978-3-030-86323-4

Electronic ISBN: 978-3-030-86324-1

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-86324-1_6

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