Infectious risk events and their novelty in event-based surveillance: new definitions and annotated corpus: Infectious risk events and their novelty in event-based surveillance: F. Delon et al.

Event-based surveillance (EBS) requires the analysis of an ever-increasing volume of documents, requiring automated processing to support human analysts. Few annotated corpora are available for the evaluation of information extraction tools in the EBS domain. The main objective of this work was to b...

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Published in:Language Resources & Evaluation Vol. 59; no. 1; pp. 277 - 296
Main Authors: Delon, François, Bédubourg, Gabriel, Bouscarrat, Léo, Meynard, Jean-Baptiste, Valois, Aude, Queyriaux, Benjamin, Ramisch, Carlos, Tanti, Marc
Format: Article
Published: Springer Nature Mar2025
Subjects:
Online Access:View this record in EBSCOhost
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        atl: Infectious risk events and their novelty in event-based surveillance: new definitions and annotated corpus: Infectious risk events and their novelty in event-based surveillance: F. Delon et al.
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          Delon, François
          Bédubourg, Gabriel
          Bouscarrat, Léo
          Meynard, Jean-Baptiste
          Valois, Aude
          Queyriaux, Benjamin
          Ramisch, Carlos
          Tanti, Marc
        affil:
          https://ror.org/0508wny29 Aix Marseille Univ, Inserm, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, ISSPAM, Marseille, France
          https://ror.org/0103yxp25 French Defense Health Service, Paris, France
          EURA NOVA, Marseille, France
          https://ror.org/0257sgk90 CNRS, LIS, Université de Toulon, Aix-Marseille Université, Marseille, France
          https://ror.org/02r084d93 Centre Hospitalier de Cayenne, Cayenne, French Guiana
          HIPS Agency GmbH, Munich, Germany
      su:
        Natural language processing
        Text mining
        Artificial intelligence
        Data mining
        Image processing
      sug:
        subj:
          Natural language processing
          Text mining
          Artificial intelligence
          Data mining
          Image processing
      keyword:
        Corpus
        Event-based surveillance
        Information and Computing Sciences Artificial Intelligence and Image Processing
        Semantics
      ab: Event-based surveillance (EBS) requires the analysis of an ever-increasing volume of documents, requiring automated processing to support human analysts. Few annotated corpora are available for the evaluation of information extraction tools in the EBS domain. The main objective of this work was to build a corpus containing documents which are representative of those collected in the current EBS information systems, and to annotate them with events and their novelty. We proposed new definitions of infectious events and their novelty suited to the background work of analysts working in the EBS domain, and we compiled a corpus of 305 documents describing 283 infectious events. There were 36 included documents in French, representing a total of 11 events, with the remainder in English. We annotated novelty for the 110 most recent documents in the corpus, resulting in 101 events. The inter-annotator agreement was 0.74 for event identification (F1-Score) and 0.69 [95% CI: 0.51; 0.88] (Kappa) for novelty annotation. The overall agreement for entity annotation was lower, with a significant variation according to the type of entities considered (range 0.30–0.68). This corpus is a useful tool for creating and evaluating algorithms and methods submitted by EBS research teams for event detection and annotation of their novelties, aiming at the operational improvement of document flow processing. The small size of this corpus makes it less suitable for training natural language processing models, although this limitation tends to fade away when using few-shots learning methods.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved.
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