Nicolas Gutehrlé ; Iana Atanassova - Processing the structure of documents: Logical Layout Analysis of historical newspapers in French

jdmdh:9093 - Journal of Data Mining & Digital Humanities, May 30, 2022, NLP4DH -
Processing the structure of documents: Logical Layout Analysis of historical newspapers in FrenchArticle

Authors: Nicolas Gutehrlé ; Iana Atanassova

    Background. In recent years, libraries and archives led important digitisation campaigns that opened the access to vast collections of historical documents. While such documents are often available as XML ALTO documents, they lack information about their logical structure. In this paper, we address the problem of Logical Layout Analysis applied to historical documents in French. We propose a rule-based method, that we evaluate and compare with two Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set contains French newspapers, periodicals and magazines, published in the first half of the twentieth century in the Franche-Comté Region. Results. Our rule-based system outperforms the two other models in nearly all evaluations. It has especially better Recall results, indicating that our system covers more types of every logical label than the other two models. When comparing RIPPER with Gradient Boosting, we can observe that Gradient Boosting has better Precision scores but RIPPER has better Recall scores. Conclusions. The evaluation shows that our system outperforms the two Machine Learning models, and provides significantly higher Recall. It also confirms that our system can be used to produce annotated data sets that are large enough to envisage Machine Learning or Deep Learning approaches for the task of Logical Layout Analysis. Combining rules and Machine Learning models into hybrid systems could potentially provide even better performances. Furthermore, as the layout in historical documents evolves rapidly, one possible solution to overcome this problem would be to apply Rule Learning algorithms to bootstrap rule sets adapted to different publication periods.

    Volume: NLP4DH
    Section: Digital humanities in languages
    Published on: May 30, 2022
    Accepted on: April 3, 2022
    Submitted on: February 17, 2022
    Keywords: Computer Science - Computation and Language

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