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

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 improving the results of the lexicosemantic representation. Our results showed no difference between the early and late fusion approaches. Temporal features performed poorly on their own. Conversely, spatial features were the most discriminative features, highlighting the need for robust methods for spatial entity extraction, disambiguation and representation in internet-based surveillance systems.