Search Results for author: Fleur Mougin

Found 5 papers, 1 papers with code

Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph

no code implementations LREC 2020 Georgeta Bordea, Stefano Faralli, Fleur Mougin, Paul Buitelaar, Gayo Diallo

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.

Knowledge Graphs

Query selection methods for automated corpora construction with a use case in food-drug interactions

no code implementations WS 2019 Georgeta Bordea, R, Tsanta riatsitohaina, Fleur Mougin, Natalia Grabar, Thierry Hamon

In this paper, we address the problem of automatically constructing a relevant corpus of scientific articles about food-drug interactions.

IAM at CLEF eHealth 2018: Concept Annotation and Coding in French Death Certificates

1 code implementation10 Jul 2018 Sébastien Cossin, Vianney Jouhet, Fleur Mougin, Gayo Diallo, Frantz Thiessard

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.

POMELO: Medline corpus with manually annotated food-drug interactions

no code implementations RANLP 2017 Thierry Hamon, Vincent Tabanou, Fleur Mougin, Natalia Grabar, Frantz Thiessard

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.

Large scale biomedical texts classification: a kNN and an ESA-based approaches

no code implementations9 Jun 2016 Khadim Dramé, Fleur Mougin, Gayo Diallo

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.

Classification General Classification +3

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