Abstract
In this paper, we present a dataset and a baseline evaluation for multilingual epidemic event extraction. We experiment with a multilingual news dataset which we annotate at the token level, a common tagging scheme utilized in event extraction systems. We approach the task of extracting epidemic events by first detecting the relevant documents from a large collection of news reports. Then, event extraction (disease names and locations) is performed on the detected relevant documents. Preliminary experiments with the entire dataset and with ground-truth relevant documents showed promising results, while also establishing a stronger baseline for epidemiological event extraction.
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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Notes
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The dataset is available at https://daniel.greyc.fr/public/index.php?a=corpus.
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XLM-RoBERTa-base was trained on 2.5TB of CommonCrawl data in 100 languages.
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Mutuvi, S., Boros, E., Doucet, A., Lejeune, G., Jatowt, A., Odeo, M. (2021). Token-Level Multilingual Epidemic Dataset for Event Extraction. In: Berget, G., Hall, M.M., Brenn, D., Kumpulainen, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2021. Lecture Notes in Computer Science(), vol 12866. Springer, Cham. https://doi.org/10.1007/978-3-030-86324-1_6
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