Renaud Lancelot


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Automated Processing of Multilingual Online News for the Monitoring of Animal Infectious Diseases
Sarah Valentin | Renaud Lancelot | Mathieu Roche
Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)

The Platform for Automated extraction of animal Disease Information from the web (PADI-web) is an automated system which monitors the web for monitoring and detecting emerging animal infectious diseases. The tool automatically collects news via customised multilingual queries, classifies them and extracts epidemiological information. We detail the processing of multilingual online sources by PADI-web and analyse the translated outputs in a case study

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Information retrieval for animal disease surveillance: a pattern-based approach.
Sarah Valentin | Mathieu Roche | Renaud Lancelot
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

Animal diseases-related news articles are richin 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.


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Monitoring Disease Outbreak Events on the Web Using Text-mining Approach and Domain Expert Knowledge
Elena Arsevska | Mathieu Roche | Sylvain Falala | Renaud Lancelot | David Chavernac | Pascal Hendrikx | Barbara Dufour
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.