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Article published In: Lingvisticæ Investigationes
Vol. 47:1 (2024) ► pp.30–67

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CRI

A Competent Reader Imitator for detecting binomial names in an historical corpus

 | Université Paris-Saclay
 | IRISA — University of Rennes
Published online: 31 October 2024
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https://doi.org/10.1075/li.00107.mor
Summary
La Nature (1873–1962) is a French popular science magazine that spanned a large time period and a large range of topics. It is available via ocerized archives so that it forms a corpus that is simultaneously diachronous, heterogeneous, and noisy. Although these characteristics make it complex to analyze, La Nature is of great interest for digital humanities studies on the evolution of thoughts in science, technology, and even politics. The work presented in this article is part of research on the semantic annotation of these archives, which is discovering clues for exploring them. One type of clue that has not been explored in a complex corpus such as La Nature is binomial names, or more specifically, the named entities that refer to the Linnean classification of life, e.g., Escherichia coli. To overcome this complexity, the concept of a Competent Reader, who can detect binomial names even when obsolete, non-standard or defaced by OCR, is introduced. By imitating a Competent Reader, our approach, which we call the Competent Reader Imitator (CRI), involves combining a rule-based approach with a frequency argument. We show that this innovative method is robust to numerous variations and consistently achieves an F-measure of about 70% despite diachronicity, heterogeneity, and noise, which are all known to impede named entity recognition. Our method has many potential applications, such as in the study of chemical names and names of scientific and technical artifacts, which could benefit from the Competent Reader imitation approach. Beyond our work on La Nature, we hope this paper provides a set of tools and methods that are easily understandable, frugal, and usable for a general public interested in exploring similar historical corpus.
Article outline
  • 1.Introduction
  • 2.Context information
    • 2.1The Linnean classification
    • 2.2The corpus of La Nature
      • 2.2.1Structure of the corpus
      • 2.2.2Volumetry of the corpus
  • 3.Creation of a gold standard
    • 3.1The Competent Reader Hypothesis — CRH
    • 3.2The task
    • 3.3The manual annotation
  • 4.State of the art and baseline
    • 4.1State of the art
    • 4.2LINNAEUS
    • 4.3TAXREF classifier
    • 4.4QUAESITOR
  • 5.A Competent Reader Imitator
    • 5.1Latin
    • 5.2A Rare-Frequent Threshold
    • 5.3Handling deviations from the code
  • 6.Evaluation
    • 6.1Comparison with the state of the art
    • 6.2Ex-post evaluation
    • 6.3Sensitivity to accepted patterns
    • 6.4Sensitivity to thematic variability
  • 7.Conclusion & future work
    • 7.1Carbon impact
    • 7.2Conclusion / discussion
    • 7.3Future work
  • References
References (55)
References
Abdalla, M. & Abdalla, M. (2021). The grey hoodie project: Big tobacco, big tech, and the threat on academic integrity. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’21, 287–297. New York, NY, USA: Association for Computing Machinery. Google Scholar logo with link to Google Scholar
Abdalla, M., Wahle, J. P., Ruas, T. L., Névéol, A., Ducel, F., Mohammad, S. M. & Fort, K. (2023). The elephant in the room: Analyzing the presence of big tech in natural language processing research. In A. Rogers et al. (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9–14, 2023, 13141–13160: Association for Computational Linguistics. Google Scholar logo with link to Google Scholar
Akella, L. M., Norton, C. & Miller, H. (2012). Netineti: Discovery of scientific names from text using machine learning methods. BMC Bioinformatics, 131, 211. Google Scholar logo with link to Google Scholar
Bánki, O., Roskov, Y., Döring, M., Ower, G., Hernández Robles, D., Plata Corredor, C., Stjernegaard Jeppesen, T., Örn, A., Vandepitte, L., Hobern, D., Schalk, P., DeWalt, R., Ma, K., Miller, J., Orrell, T., Aalbu, R., Abbott, J., Adlard, R. & Adriaenssens, E. e. a. (2023). Catalogue of Life Checklist.Google Scholar logo with link to Google Scholar
Bannour, N., Ghannay, S., Névéol, A. & Ligozat, A. (2021). Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools. In N. S. Moosavi et al. (Eds.), Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing, SustaiNLP@EMNLP 2021, Virtual, November 10, 2021, 11–21: Association for Computational Linguistics. Google Scholar logo with link to Google Scholar
Barrière, C. (2016). Natural Language Understanding in a Semantic Web Context. Springer. Google Scholar logo with link to Google Scholar
Becker, C. (2023). Insolvent: How to Reorient Computing for Just Sustainability. Cambridge: The MIT Press. Google Scholar logo with link to Google Scholar
Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, 610–623. New York, NY, USA: Association for Computing Machinery. Google Scholar logo with link to Google Scholar
Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R. & Bao, M. (2022). The values encoded in machine learning research. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, 173–184. New York, NY, USA: Association for Computing Machinery. Google Scholar logo with link to Google Scholar
Burdick, A., Drucker, J., Lunenfeld, P., Presner, T. & Schnapp, J. (2012). Digital Humanities. The MIT Press.Google Scholar logo with link to Google Scholar
Castellan, S., Käfer, J. & Tannier, E. (2023). Back to the trees: Identifying plants with Human Intelligence. In Ninth Computing within Limits 2023: LIMITS. [URL].
Clark, A., Fox, C. & Lappin, S. (2012). The handbook of computational linguistics and natural language processing, volume 1181. John Wiley & Sons.Google Scholar logo with link to Google Scholar
CNUM (ca. 2000). Conservatoire numérique des Arts et Métiers. HTTP links to scanned fac-simile of LA NATURE: [URL]
COMETS, Ethics Committee of the CNRS (2022). AVIS n 2022–43, Intégrer les enjeux environnementaux à  la conduite de la recherche — Une responsabilité éthique.Google Scholar logo with link to Google Scholar
Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., Viegas, F., França, C., Martins, W. S., Almeida, J. M., Rosa, T., Rocha, L. & Gonçalves, M. A. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study. Information Processing & Management, 58(3), 102481. Google Scholar logo with link to Google Scholar
DeMillo, R., Lipton, R. & Sayward, F. (1978). Hints on test data selection: Help for the practicing programmer. Computer, 11(4), 34–41. Google Scholar logo with link to Google Scholar
Devlin, J., Chang, M., Lee, K. & Toutanova, K. (2019). BERT: pre-training of deep bidirectional transformers for language understanding.Google Scholar logo with link to Google Scholar
Ehrmann, M., Hamdi, A., Pontes, E. L., Romanello, M. & Doucet, A. (2021). Named entity recognition and classification on historical documents: A survey. CoRR, abs/2109.11406.Google Scholar logo with link to Google Scholar
Eltyeb, S. & Salim, N. (2014). Chemical named entities recognition: a review on approaches and applications. Cheminform. 6:(17).Google Scholar logo with link to Google Scholar
Gabrys, J., Pritchard, H. & Barratt, B. (2016). Just good enough data: Figuring data citizenships through air pollution sensing and data stories. Big Data & Society, 3(2), 2053951716679677. Google Scholar logo with link to Google Scholar
Gargominy, O., Tercerie, S., Régnier, C., Ramage, T., Dupont, P., Daszkiewicz, P. & Poncet, L. (2021). TAXREF v15, référentiel taxonomique pour la France : méthodologie, mise en œuvre et diffusion.Google Scholar logo with link to Google Scholar
Gerner, M., Nenadic, G. & Bergman, C. M. (2010). Linnaeus: a species name identification system for biomedical literature. BMC Bioinformatics, 11(1), 1–17. Google Scholar logo with link to Google Scholar
Gundersen, O. E., Gil, Y. & Aha, D. W. (2018). On reproducible AI: Towards reproducible research, open science, and digital scholarship in AI publications. AI Magazine, 391. Google Scholar logo with link to Google Scholar
Gupta, U., Kim, Y. G., Lee, S., Tse, J., Lee, H.-H. S., Wei, G.-Y., Brooks, D. & Wu, C.-J. (2020). Chasing carbon: The elusive environmental footprint of computing.Google Scholar logo with link to Google Scholar
Jurafsky, D. & Martin, J. H. (2009). Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition. Pearson Prentice Hall.Google Scholar logo with link to Google Scholar
Koning, D., Sarkar, I. N. & Moritz, T. (2005). Taxongrab: Extracting taxonomic names from text. Biodiversity Informatics, 21, 79–82. Google Scholar logo with link to Google Scholar
Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.Google Scholar logo with link to Google Scholar
Labusch, K., Neudecker, C. & Zellhofer, D. (2019). Bert for named entity recognition in contemporary and historic german. In Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019): Long Papers, 1–9. Erlangen, Germany: German Society for Computational Linguistics & Language Technology.Google Scholar logo with link to Google Scholar
Lannelongue, L., Grealey, J. & Inouye, M. (2021). Green algorithms: Quantifying the carbon footprint of computation. Advanced Science, 8(12), 2100707. Google Scholar logo with link to Google Scholar
Le, H., Vial, L., Frej, J., Segonne, V., Coavoux, M., Lecouteux, B., Allauzen, A., Crabbé, B., Besacier, L. & Schwab, D. (2020). Flaubert: Unsupervised language model pre-training for french. In N. Calzolari et al. (Eds.), Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11–16, 2020, 2479–2490: European Language Resources Association.Google Scholar logo with link to Google Scholar
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H. & Kang, J. (2019). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240. Google Scholar logo with link to Google Scholar
Little, D. (2020). Recognition of Latin scientific names using artificial neural networks. Applications in Plant Sciences, 81. Google Scholar logo with link to Google Scholar
Luccioni, A. S., Viguier, S. & Ligozat, A.-L. (2023). Estimating the carbon footprint of BLOOM, a 176b parameter language model. Journal of Machine Learning Research, 24(253), 1–15.Google Scholar logo with link to Google Scholar
Martin, L., Muller, B., Ortiz Suárez, P. J., Dupont, Y., Romary, L., de la Clergerie, É., Seddah, D. & Sagot, B. (2020). CamemBERT: a tasty French language model. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7203–7219. Online: Association for Computational Linguistics. Google Scholar logo with link to Google Scholar
Morand, C. & Ridoux, O. (2023). Extraction dans des textes anciens d’entités nommées de type binômes de la classification linnéenne du vivant : une étude de cas. Revue des Nouvelles Technologies de l’Information, Extraction et Gestion des Connaissances, RNTI-E-39, 417–424.Google Scholar logo with link to Google Scholar
Mozzherin, D., Myltsev, A. & Patterson, D. (2017). “gnparser”: A powerful parser for scientific names based on parsing expression grammar. BMC Bioinformatics, 181. Google Scholar logo with link to Google Scholar
Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticæ Investigationes, 301, 3–26. Google Scholar logo with link to Google Scholar
Nasar, Z., Jaffry, S. W. & Malik, M. (2021). Named entity recognition and relation extraction: State of the art. ACM Computing Surveys, 541.Google Scholar logo with link to Google Scholar
NCBI (2008). The national center for biotechnology information taxonomy.Google Scholar logo with link to Google Scholar
Nédellec, C., Bessières, P., Bossy, R. R., Kotoujansky, A. & Manine, A.-P. (2006). Annotation guidelines for machine learning-based named entity recognition in microbiology. In Proceeding of Data and Text Mining for Integrative Biology Workshop 17.European Conference on Machine Learning 10. European Conference on Principles and Practice of Knowledge Discovery in Databases, Workshop on data and text mining for integrative biology. Springer.
Nguyen, N. T. H., Gabud, R. & Ananiadou, S. (2019). Copious: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature. Biodiversity Data Journal. Google Scholar logo with link to Google Scholar
Pafilis, E., Frankild, S. P., Fanini, L., Faulwetter, S., Pavloudi, C., Vasileiadou, A., Arvanitidis, C. & Jensen, L. J. (2013). The species and organisms resources for fast and accurate identification of taxonomic names in text. PloS one, 8(6), e65390. Google Scholar logo with link to Google Scholar
Sacco, G. M. & Tzitzikas, Y. (Eds.) (2009). Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience, volume 251 of The Information Retrieval Series. Springer. Google Scholar logo with link to Google Scholar
Santarius, T., Bieser, J. C. T., Frick, V., Höjer, M., Gossen, M., Hilty, L. M., Kern, E., Pohl, J., Rohde, F. & Lange, S. (2022). Digital sufficiency: conceptual considerations for icts on a finite planet. Annals of Telecommunications, 78(5–6), 277–295. Google Scholar logo with link to Google Scholar
Sautter, G., Böhm, K. & Agosti, D. (2006). A combining approach to find all taxon names (FAT). Biodiversity Informatics, 31. Google Scholar logo with link to Google Scholar
Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. (2020). Green AI. Commun. ACM, 63(12), 54–63. Google Scholar logo with link to Google Scholar
Seideh, M. A. F., Fehri, H. & Haddar, K. (2016). Named entity recognition from arabic-french herbalism parallel corpora. In T. Okrut et al. (Eds.), Automatic Processing of Natural-Language Electronic Texts with NooJ, 191–201. Cham: Springer International Publishing. Google Scholar logo with link to Google Scholar
Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M. & Villalobos, P. (2022). Compute trends across three eras of machine learning. In 2022 International Joint Conference on Neural Networks (IJCNN), 1–8. Google Scholar logo with link to Google Scholar
Smil, V. (2021). Grand Transitions: How the Modern World Was Made. Oxford: OUP. Google Scholar logo with link to Google Scholar
Strubell, E., Ganesh, A. & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. Florence, Italy: Association for Computational Linguistics. Google Scholar logo with link to Google Scholar
Thompson, N., Greenewald, K., Lee, K. & Manso, G. F. (2023). The Computational Limits of Deep Learning. In Ninth Computing within Limits 2023: LIMITS. [URL].
Tissandier, G. (1873–1962). LA NATURE : Revue des sciences et de leurs applications aux arts et à l’industrie.Google Scholar logo with link to Google Scholar
Turland, N. J., Wiersema, J. H., Barrie, F. R., Greuter, W., Hawksworth, D. L., Herendeen, P. S., Knapp, S., Kusber, W.-H., Li, D.-Z., Marhold, K., May, T. W., McNeill, J., Monro, A. M., Prado, J., Price, M. J. & Smith, G. F. (Eds.) (2018). International Code of Nomenclature for algae, fungi, and plants (Shenzhen Code). Glashütten: Koeltz Botanical Books. Google Scholar logo with link to Google Scholar
Vautrin, G. (2018). Histoire de la vulgarisation scientifique avant 1900 (History of Science Popularization before 1900 — in France). EDP sciences.Google Scholar logo with link to Google Scholar
Yu, P. & Wang, X. (2020). Bert-based named entity recognition in chinese twenty-four histories. In G. Wang et al. (Eds.), Web Information Systems and Applications, 289–301. Cham: Springer International Publishing. Google Scholar logo with link to Google Scholar
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