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Injecting Temporal-Aware Knowledge in Historical Named Entity Recognition

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13980))

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Abstract

In this paper, we address the detection of named entities in multilingual historical collections. We argue that, besides the multiple challenges that depend on the quality of digitization (e.g., misspellings and linguistic errors), historical documents can pose another challenge due to the fact that such collections are distributed over a long enough period of time to be affected by changes and evolution of natural language. Thus, we consider that detecting entities in historical collections is time-sensitive, and explore the inclusion of temporality in the named entity recognition (NER) task by exploiting temporal knowledge graphs. More precisely, we retrieve semantically-relevant additional contexts by exploring the time information provided by historical data collections and include them as mean-pooled representations in a Transformer-based NER model. We experiment with two recent multilingual historical collections in English, French, and German, consisting of historical newspapers (19C-20C) and classical commentaries (19C). The results are promising and show the effectiveness of injecting temporal-aware knowledge into the different datasets, languages, and diverse entity types.

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Notes

  1. 1.

    https://gallica.bnf.fr/.

  2. 2.

    https://trove.nla.gov.au/.

  3. 3.

    The code is available at https://github.com/EmanuelaBoros/clef-hipe-2022-l3i.

  4. 4.

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

  5. 5.

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

  6. 6.

    https://deepgraphlearning.github.io/project/wikidata5m.

  7. 7.

    https://www.wikidata.org/.

  8. 8.

    https://github.com/mniepert/mmkb/tree/master/TemporalKGs/wikidata.

  9. 9.

    https://www.elastic.co/guide/en/elasticsearch/reference/8.1/release-highlights.html.

  10. 10.

    We leave out the details that can be consulted in [45].

  11. 11.

    We do not utilize in this case the additional Transformer layers with adapters, since these were specifically proposed for noisy/non-standard text and they do not bring any increase in performance on standard text [4].

  12. 12.

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

  13. 13.

    https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2.

  14. 14.

    We utilized the HIPE-scorer https://github.com/hipe-eval/HIPE-scorer.

  15. 15.

    We would expect higher results by utilising the temporal information, however, for this experimental setup, we were limited in terms of resources.

  16. 16.

    https://mromanello.github.io/ajax-multi-commentary/.

  17. 17.

    Although the exact date of its first performance is unknown, most scholars date it to relatively early in Sophocles’ career (possibly the earliest Sophoclean play still in existence), somewhere between 450 BCE to 430 BCE, possibly around 444 BCE.

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Acknowledgements

This work has been supported by the ANNA (2019-1R40226) and TERMITRAD (2020–2019-8510010) projects funded by the Nouvelle-Aquitaine Region, France.

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Authors and Affiliations

  1. University of La Rochelle, L3i, 17000, La Rochelle, France

    Carlos-Emiliano González-Gallardo, Emanuela Boros, Edward Giamphy, Ahmed Hamdi, José G. Moreno & Antoine Doucet

  2. Preligens, 75009, Paris, France

    Edward Giamphy

  3. University of Toulouse, IRIT, 31000, Toulouse, France

    José G. Moreno

Authors
  1. Carlos-Emiliano González-Gallardo
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  2. Emanuela Boros
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  3. Edward Giamphy
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  4. Ahmed Hamdi
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  5. José G. Moreno
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  6. Antoine Doucet
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Correspondence to Carlos-Emiliano González-Gallardo .

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Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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González-Gallardo, CE., Boros, E., Giamphy, E., Hamdi, A., Moreno, J.G., Doucet, A. (2023). Injecting Temporal-Aware Knowledge in Historical Named Entity Recognition. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_24

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