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Monitoring disease outbreak events on the web using text-mining approach and domain expert knowledge

Arsevska E., Roche M., Falala S., Lancelot R., Chavernac D., Hendrikx P., Dufour B.. 2016. In : Calzolari Nicoletta (ed.), Choukri Khalid (ed.) , Declerck Thierry (ed.) , Grobelnik Marko (ed.), Maegaard Bente (ed.), Mariani Joseph (ed.) , Moreno Asuncion (ed.), Odijk Jan (ed.), Piperidis Stelios (ed.). LREC 2016 Proceedings. Paris : ELRA, p. 3407-3411. International Conference on Language Resources and Evaluation. 10, 2016-05-23/2016-05-28, Portoroz (Slovénie).

Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. We propose a generic method to enrich and extend the use of different expressions as queries in order to improve the acquisition of relevant disease related pages on the web. Our method combines a text mining approach to extract terms from corpora of relevant disease outbreak documents, and domain expert elicitation (Delphi method) to propose expressions and to select relevant combinations between terms obtained with text mining. In this paper we evaluated the performance as queries of a number of expressions obtained with text mining and validated by a domain expert and expressions proposed by a panel of 21 domain experts. We used African swine fever as an infectious animal disease model. The expressions obtained with text mining outperformed as queries the expressions proposed by domain experts. However, domain experts proposed expressions not extracted automatically. Our method is simple to conduct and flexible to adapt to any other animal infectious disease and even in the public health domain. (Résumé d'auteur)

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