Automatic Conceptual Indexing of French Pharmaceutical Theses

French pharmaceutical theses are rarely quoted. If the main obstacles originate from language or access barriers, proper indexation could also be blamed. Manually extracted key-words don't necessary come from a structured thesaurus. In the following work, this manual indexing method is compared to an automated one, “Nomindex”, based on UMLS. The automated method is improved by the addition of a relevance scoring system. The first indexing step consists of downloading, adapting and indexing theses in electronic format. Results will then be analyzed and sorted by relevance, through the comparison of classic statistical indices (noise, silence and relevance). It was assumed that the manually obtained key-words were always relevant. The silence of manual indexing is nevertheless high: seven new key-words are proposed by Nomindex, which results are mixed (10 % of silence, but 50 % of noise). These results are promising on the first experiment on pharmaceutical document without lexicon improvement. The indexing, if it is currently insufficient for a real life use, could easily be improved by specific updates of the lexicon.