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European Conference on Information Retrieval

ECIR 2022: Advances in Information Retrieval pp 347–354Cite as

Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13186)

Abstract

We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. HIPE-2022 is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of the evaluation lab is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets.

Keywords

  • Named entity processing
  • Information extraction
  • Text understanding
  • Historical documents
  • Digital humanities

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Notes

  1. 1.

    https://impresso.github.io/CLEF-HIPE-2020.

  2. 2.

    https://hipe-eval.github.io/HIPE-2022/.

  3. 3.

    Classical commentaries are scholarly publications dedicated to the in-depth analysis and explanation of ancient literary works. As such, they aim to facilitate the reading and understanding of a given literary text. More information on the HIPE-2022 classical commentaries corpus in Sect. 3.2.

  4. 4.

    https://www.newseye.eu/.

  5. 5.

    https://sonar.fh-potsdam.de/.

  6. 6.

    https://livingwithmachines.ac.uk/.

  7. 7.

    Impresso [4] and SoNAR guidelines [12] were derived from Quaero guidelines [16], while NewsEye guidelines correspond to a subset of the impresso guidelines.

  8. 8.

    https://github.com/impresso/CLEF-HIPE-2020-scorer.

References

  1. Beryozkin, G., Drori, Y., Gilon, O., Hartman, T., Szpektor, I.: A joint named-entity recognizer for heterogeneous tag-sets using a tag hierarchy. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 140–150, Florence, Italy, July 2019. https://aclanthology.org/P19-1014

  2. Coll Ardanuy, M., Beavan, D., Beelen, K., Hosseini, K., Lawrence, J.: Dataset for Toponym Resolution in Nineteenth-Century English Newspapers (2021). https://doi.org/10.23636/b1c4-py78

  3. Ehrmann, M., Colavizza, G., Rochat, Y., Kaplan, F.: Diachronic evaluation of NER systems on old newspapers. In: Proceedings of the 13th Conference on Natural Language Processing (KONVENS 2016), pp. 97–107, Bochum (2016). Bochumer Linguistische Arbeitsberichte. https://infoscience.epfl.ch/record/221391

  4. Ehrmann, M., Romanello, M., Flückiger, A., Clematide, S.: Impresso Named Entity Annotation Guidelines. Annotation guidelines, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Zurich University (UZH), January 2020. https://zenodo.org/record/3585750

  5. Ehrmann, M., Romanello, M., Flückiger, A., Clematide, S.: Extended Overview of CLEF HIPE 2020: named entity processing on historical newspapers. In: Cappellato, L., Eickhoff, C., Ferro, N., Névéol, A., (eds.), Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum, vol. 2696, p. 38, Thessaloniki, Greece (2020). CEUR-WS. https://doi.org/10.5281/zenodo.4117566, https://infoscience.epfl.ch/record/281054

  6. Ehrmann, M., Hamdi, A., Pontes, E.L., Romanello, M., Doucet, A.: Named Entity Recognition and Classification on Historical Documents: A Survey. arXiv:2109.11406 [cs], September 2021

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  12. Menzel, S., Zinck, J., Schnaitter, H., Petras, V.: Guidelines for Full Text Annotations in the SoNAR (IDH) Corpus. Technical report, Zenodo, July 2021. https://zenodo.org/record/5115933

  13. Padilla, T.: Responsible Operations: Data Science, Machine Learning, and AI in Libraries. Technical report, OCLC Research, USA, May 2020. https://www.oclc.org/content/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html

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  15. Matteo, R., Sven, N.-M., Bruce, R.: Optical character recognition of 19th century classical commentaries: the current state of affairs. In: The 6th International Workshop on Historical Document Imaging and Processing (HIP 2021), Lausanne, September 2021. Association for Computing Machinery. https://doi.org/10.1145/3476887.3476911

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Acknowledgements

We are grateful to the research project consortia and teams who kindly accepted to retain the publication of part of their NE-annotated datasets to support HIPE-2022: the NewsEye project (The NewsEye project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 770299); the Living with Machine project, in particular Mariona Coll’Ardanuy; and the SoNAR project, in particular Clemens Neudecker. We also thank Sally Chambers, Clemens Neudecker and Frédéric Kaplan for their support and guidance as part of the lab’s advisory board.

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Affiliations

  1. Digital Humanities Laboratory, EPFL, Vaud, Switzerland

    Maud Ehrmann

  2. University of Lausanne, Lausanne, Switzerland

    Matteo Romanello

  3. University of La Rochelle, La Rochelle, France

    Antoine Doucet

  4. Department of Computational Linguistics, University of Zurich, Zurich, Switzerland

    Simon Clematide

Authors
  1. Maud Ehrmann
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  2. Matteo Romanello
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  3. Antoine Doucet
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  4. Simon Clematide
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Corresponding author

Correspondence to Maud Ehrmann .

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Ehrmann, M., Romanello, M., Doucet, A., Clematide, S. (2022). Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents. In: , et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_44

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  • DOI: https://doi.org/10.1007/978-3-030-99739-7_44

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