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2021 | OriginalPaper | Chapter

Token-Level Multilingual Epidemic Dataset for Event Extraction

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

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.

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previous chapter Terminology/Keyphrase Extraction for Creation of Book Indexes in Polish

next chapter A Semantic Search Engine for Historical Handwritten Document Images

The dataset is available at https://daniel.greyc.fr/public/index.php?a=corpus.

https://catalog.ldc.upenn.edu/LDC2006T06.

https://catalog.ldc.upenn.edu/LDC2020T13.

https://github.com/doccano/doccano.

The hyperparameters for both models are detailed in the papers [7, 10].

https://huggingface.co/bert-base-multilingual-cased.

https://huggingface.co/bert-base-multilingual-uncased.

XLM-RoBERTa-base was trained on 2.5TB of CommonCrawl data in 100 languages.

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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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