Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model

Abstract : This paper proposes a new approach for indexing biomedical documents based on the combination of a Possibilistic Network and a Vector Space Model. This later carries out partial matching between documents and biomedical vocabularies. The main contribution of the proposed approach is to combine the cosine similarity and the two measures of possibility and necessity to enhance the estimation of the similarity between a document and a given concept. The possibility estimates the extent to which a document is not similar to the concept. The necessity allows the confirmation that the document is similar to the concept. Experiments were carried out on the OSHUMED corpora and showed encouraging results.
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Communication dans un congrès
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02100421
Contributeur : Lina F Soualmia <>
Soumis le : lundi 15 avril 2019 - 19:32:01
Dernière modification le : mercredi 15 mai 2019 - 03:39:24

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Wiem Chebil, Lina Fatima Soualmia, Mohamed Nazih Omri, Stéfan Jacques Darmoni. Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model. Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, Jun 2015, Pavia, Italy. pp.227-231, ⟨10.1007/978-3-319-19551-3_29⟩. ⟨hal-02100421⟩

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