Valentin Sarah, Lancelot Renaud, Roche Mathieu.
2020. Information retrieval for animal disease surveillance: a pattern-based approach.
In : Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. Holderness Eben (ed.), Yepes Antonio Jimeno (ed.), Lavelli Alberto (ed.), Lavelli Anne-Lyse (ed.), Pustejovsky James (ed.), Rinaldi Fabio (ed)
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Version publiée
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Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/YGAKNB / Url - éditeur : https://www.aclweb.org/anthology/volumes/2020.louhi-1/
Note générale : Le congrès s'est tenu en ligne
Résumé : Animal diseases-related news articles are rich in information useful for risk assessment. In this paper, we explore a method to automatically retrieve sentence-level epidemiological information. Our method is an incremental approach to create and expand patterns at both lexical and syntactic levels. Expert knowledge input are used at different steps of the approach. Distributed vector representations (word embedding) were used to expand the patterns at the lexical level, thus alleviating manual curation. We showed that expert validation was crucial to improve the precision of automatically generated patterns.
Mots-clés libres : Epidemic intelligence, Text Mining, Information retrieval, Animal disease surveillance
Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context
Auteurs et affiliations
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Valentin Sarah, CIRAD-BIOS-UMR ASTRE (FRA)
ORCID: 0000-0002-9028-681X
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Lancelot Renaud, CIRAD-BIOS-UMR ASTRE (FRA) ORCID: 0000-0002-5826-5242
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Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
Source : Cirad-Agritrop (https://agritrop.cirad.fr/597052/)
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