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2021 | OriginalPaper | Chapter

Multilingual Epidemic Event Extraction

Authors : Stephen Mutuvi, Emanuela Boros, Antoine Doucet, Gaël Lejeune, Adam Jatowt, Moses Odeo

Published in: Towards Open and Trustworthy Digital Societies

Publisher: Springer International Publishing

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Abstract

In this paper, we focus on epidemic event extraction in multilingual and low-resource settings. The task of extracting epidemic events is defined as the detection of disease names and locations in a document. We experiment with a multilingual dataset comprising news articles from the medical domain with diverse morphological structures (Chinese, English, French, Greek, Polish, and Russian). We investigate various Transformer-based models, also adopting a two-stage strategy, first finding the documents that contain events and then performing event extraction. Our results show that error propagation to the downstream task was higher than expected. We also perform an in-depth analysis of the results, concluding that different entity characteristics can influence the performance. Moreover, we perform several preliminary experiments for the low-resourced languages present in the dataset using the mean teacher semi-supervised technique. Our findings show the potential of pre-trained language models benefiting from the incorporation of unannotated data in the training process.

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previous chapter Automatic Cause-Effect Relation Extraction from Dental Textbooks Using BERT

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

The token-level annotated dataset is available at https://bit.ly/3kUQcXD.

For this model, we used the parameters recommended in [11].

https://huggingface.co/bert-base-multilingual-cased. This model was pre-trained on the top 104 languages having the largest Wikipedia edition using a masked language modeling (MLM) objective.

https://huggingface.co/bert-base-multilingual-uncased. This model was pre-trained on the top 102 languages having the largest Wikipedia editions using a masked language modeling (MLM) objective.

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

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Title: Multilingual Epidemic Event Extraction
Authors: Stephen Mutuvi
Emanuela Boros
Antoine Doucet
Gaël Lejeune
Adam Jatowt
Moses Odeo
Publisher: Springer International Publishing
Book: Towards Open and Trustworthy Digital Societies
Print ISBN: 978-3-030-91668-8

Electronic ISBN: 978-3-030-91669-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-91669-5_12

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