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In the medical field, scientific articles represent a very important source of knowledge for researchers of this domain. But due to the large volume of scientific articles published on the web, an efficient detection and use of this knowledge is quite a difficult task. In this paper, we propose our contribution for conceptual indexing of medical articles by using the MeSH (Medical Subject Headings) thesaurus. With this in mind, we propose a tool for indexing medical articles called BIOINSY (BIOmedical Indexing SYstem) which uses a language model for selecting the best representative descriptors for each document. The proposed indexing approach was evaluated by intensive experiments. These experiments were conducted on document test collections of real world clinical extracted from scientific collections, namely PUBMED and CLEF. The results generated by these experiments demonstrate the effectiveness of our indexing approach.

Biomedical indexing and retrieval system based on language modeling approach