Clement Jonquet

Also published as: Clément Jonquet


2017

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Enrichment of French Biomedical Ontologies with UMLS Concepts and Semantic Types for Biomedical Named Entity Recognition Though Ontological Semantic Annotation
Andon Tchechmedjiev | Clément Jonquet
Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop (LOTKS 2017)

2016

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Automatic Biomedical Term Polysemy Detection
Juan Antonio Lossio-Ventura | Clement Jonquet | Mathieu Roche | Maguelonne Teisseire
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Polysemy is the capacity for a word to have multiple meanings. Polysemy detection is a first step for Word Sense Induction (WSI), which allows to find different meanings for a term. The polysemy detection is also important for information extraction (IE) systems. In addition, the polysemy detection is important for building/enriching terminologies and ontologies. In this paper, we present a novel approach to detect if a biomedical term is polysemic, with the long term goal of enriching biomedical ontologies. This approach is based on the extraction of new features. In this context we propose to extract features following two manners: (i) extracted directly from the text dataset, and (ii) from an induced graph. Our method obtains an Accuracy and F-Measure of 0.978.

2014

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Automatic Term Extraction Combining Different Information (Extraction automatique de termes combinant différentes informations) [in French]
Juan Antonio Lossio-Ventura | Clement Jonquet | Mathieu Roche | Maguelonne Teisseire
Proceedings of TALN 2014 (Volume 2: Short Papers)