In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies.
In this paper, we address the problem of automatically constructing a relevant corpus of scientific articles about food-drug interactions.
In this paper, we describe the approach and results for our participation in the task 1 (multilingual information extraction) of the CLEF eHealth 2018 challenge.
When patients take more than one medication, they may be at risk of drug interactions, which means that a given drug can cause unexpected effects when taken in combination with other drugs.
Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach. ResultsExperimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0. 55% while the ESA-based approach surprisingly yielded reserved results. ConclusionsWe have proposed simple classification methods suitable to annotate textual documents using only partial information.