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Found 5 papers, 1 papers with code
IAM at CLEF eHealth 2018: Concept Annotation and Coding in French Death Certificates
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.
Large scale biomedical texts classification: a kNN and an ESA-based approaches
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.
POMELO: Medline corpus with manually annotated food-drug interactions
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.
Query selection methods for automated corpora construction with a use case in food-drug interactions
In this paper, we address the problem of automatically constructing a relevant corpus of scientific articles about food-drug interactions.
Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph
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.
