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Fusion of spatiotemporal and thematic features of textual data for animal disease surveillance

Information Processing in Agriculture 2023年第3期10卷 347-360页

作者： Sarah Valentin Renaud Lancelot Mathieu Roche CIRAD F-34398 MontpellierFrance ASTRE Univ MontpellierCIRADINRAEMontpellierFrance tetis Univ MontpellierAgroParisTechCIRADCNRSINRAEMontpellierFrance Departement de Biologie Universite de SherbrookeSherbrookeQuebecCanada

Several internet-based surveillance systems have been created to monitor the web for animal health surveillance.These systems collect a large amount of news dealing with outbreaks related to animal diseases.Automatically identifying news articles that describe the same outbreak event is a key step to quickly detect relevant epidemiological information while alleviating manual curation of news content.This paper addresses the task of retrieving news articles that are related in epidemiological terms.We tackle this issue using text mining and feature fusion methods.The main objective of this paper is to identify a textual representation in which two articles that share the same epidemiological content are close.We compared two types of representations(i.e.,features)to represent the documents:(i)morphosyntactic features(i.e.,selection and transformation of all terms from the news,based on classical textual processing steps)and(ii)lexicosemantic features(i.e.,selection,transformation and fusion of epidemiological terms including diseases,hosts,locations and dates).We compared two types of term weighing(i.e.,Boolean and TF-IDF)for both representations.To combine and transform lexicosemantic features,we compared two data fusion techniques(i.e.,early fusion and late fusion)and the effect of features generalisation,while evaluating the relative importance of each type of feature.We conducted our analysis using a corpus composed of a subset of news articles in English related to animal disease outbreaks.Our results showed that the combination of relevant lexicosemantic(epidemiological)features using fusion methods improves classical morphosyntactic representation in the context of disease-related news retrieval.The lexicosemantic representation based on TF-IDF and feature generalisation(F-measure=0.92,r-precision=0.58)outperformed the morphosyntactic representation(F-measure=0.89,r-precision=0.45),while reducing the features space.Converting the features into lower granular features(i.e.,generalisation)contributed to impr

关键词： Animal disease surveillance Text mining Ranking Fusion

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Identifying associations between epidemiological entities in news data for animal disease surveillance

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Artificial Intelligence in Agriculture 2021年第1期5卷 163-174页

作者： Sarah Valentin Renaud Lancelot Mathieu Roche CIRAD UMR ASTREUMR TETISF-34398 MontpellierFrance ASTRE Univ MontpellierCIRADINRAEMontpellierFrance tetis Univ MontpellierAgroParisTechCIRADCNRSINRAEMontpellierFrance

Event-based surveillance systems are at the crossroads of human and animal(and plant and ecosystem)health,epidemiology,statistics,and informatics.Thus,their deployment faces many challenges specific to each domain and their intersections,such as relations among automation,artificial intelligence,and expertise.In this context,ourwork pertins to the extraction of epidemiological events in textual data(i.***.news)by unsupervised methods.We define the event extraction task as detecting pairs of epidemiological entities(e.g.a disease name and location).The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics.We used a publicly available annotated corpus of 438 documents(i.***.news articles)related to animal disease events.The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall.Our results showed that using a window of words outperformed document-based and sentence-based approaches,while reducing the probability of detecting false pairs.Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework.We believe that Mutual Information would be more relevant for rare pair detection(i.***.weak signals),but requires higher manual curation to avoid false positive extraction pairs.Moreover,generalising the country-level spatial features enabled better discrimination(i.***.ranking)of relevant disease-location pairs for event extraction.

关键词： Animal disease surveillance Text mining Event extraction

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