Skip to main content
SpringerLink
Log in

Token-Level Multilingual Epidemic Dataset for Event Extraction

  • Conference paper
  • First Online:
Linking Theory and Practice of Digital Libraries (TPDL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12866))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

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

  2. 2.

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

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

References

  1. Aiello, A.E., Renson, A., Zivich, P.N.: Social media-and internet-based disease surveillance for public health. Annu. Rev. Public Health 41, 101–118 (2020)

    Article  Google Scholar 

  2. Brixtel, R., Lejeune, G., Doucet, A., Lucas, N.: Any language early detection of epidemic diseases from web news streams. In: 2013 IEEE International Conference on Healthcare Informatics, pp. 159–168. IEEE (2013)

    Google Scholar 

  3. Choi, J., Cho, Y., Shim, E., Woo, H.: Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health 16(1), 1–10 (2016). https://doi.org/10.1186/s12889-016-3893-0

    Article  Google Scholar 

  4. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)

    Article  Google Scholar 

  5. Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5–10 July 2020, pp. 8440–8451. Association for Computational Linguistics (2020). https://www.aclweb.org/anthology/2020.acl-main.747/

  6. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis (2019). https://doi.org/10.18653/v1/N19-1423

  7. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2016)

    Google Scholar 

  8. Lampos, V., Zou, B., Cox, I.J.: Enhancing feature selection using word embeddings: The case of flu surveillance. In: Proceedings of the 26th International Conference on World Wide Web, pp. 695–704 (2017)

    Google Scholar 

  9. Lejeune, G., Brixtel, R., Doucet, A., Lucas, N.: Multilingual event extraction for epidemic detection. Artif. Intell. Med. 65, 131–143 (2015). https://doi.org/10.1016/j.artmed.2015.06.005

    Article  Google Scholar 

  10. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1064–1074. Association for Computational Linguistics, Berlin (2016). https://doi.org/10.18653/v1/P16-1101. https://www.aclweb.org/anthology/P16-1101

  11. Ng, V., Rees, E.E., Niu, J., Zaghool, A., Ghiasbeglou, H., Verster, A.: Application of natural language processing algorithms for extracting information from news articles in event-based surveillance. Can. Commun. Dis. Rep. 46(6), 186–191 (2020)

    Article  Google Scholar 

  12. Wang, C.K., Singh, O., Tang, Z.L., Dai, H.J.: Using a recurrent neural network model for classification of tweets conveyed influenza-related information. In: Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017), pp. 33–38 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Multimedia University, Nairobi, Kenya

    Stephen Mutuvi & Moses Odeo

  2. University of La Rochelle, La Rochelle, France

    Stephen Mutuvi, Emanuela Boros & Antoine Doucet

  3. Sorbonne University, Paris, France

    Gaël Lejeune

  4. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

Authors
  1. Stephen Mutuvi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emanuela Boros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antoine Doucet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gaël Lejeune
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adam Jatowt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Moses Odeo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Gerd Berget

  2. The Open University, Milton Keynes, UK

    Mark Michael Hall

  3. Martin Luther University Halle-Wittenberg, Halle, Germany

    Daniel Brenn

  4. Tampere University, Tampere, Finland

    Sanna Kumpulainen

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86324-1_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86323-4

  • Online ISBN: 978-3-030-86324-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation