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Relation extraction with accurate precision is still a challenge whenprocessing full text databases. We propose an approach based on cooccurrenceanalysis in each document for which we used document organization to improveaccuracy of relation extraction. This approach is implemented in a R packagecalled \emph{x.ent}. Another facet of extraction relies on use of extractedrelation into a querying system for expert end-users. Two datasets had beenused. One of them gets interest from specialists of epidemiology in planthealth. For this dataset usage is dedicated to plant-disease explorationthrough agricultural information news. An open-data platform exploits exportsfrom \emph{x.ent} and is publicly available.

x.ent: R Package for Entities and Relations Extraction based on Unsupervised Learning and Document Structure