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Section: Application Domains

Textual Database Management

Searching in large textual corpora has already been the topic of many researches. The current stakes are the management of very large volumes of data, the possibility to answer requests relating more on concepts than on simple inclusions of words in the texts, and the characterization of sets of texts.

We work on the exploitation of scientific bibliographical bases. The explosion of the number of scientific publications makes the retrieval of relevant data for a researcher a very difficult task. The generalization of document indexing in data banks did not solve the problem. The main difficulty is to choose the keywords which will encircle a domain of interest. The statistical method used, the factorial analysis of correspondences, makes it possible to index the documents or a whole set of documents and to provide the list of the most discriminating keywords for these documents. The index validation is carried out by searching information in a database more general than the one used to build the index and by studying the retrieved documents. That in general makes it possible to still reduce the subset of words characterizing a field.

We also explore scientific documentary corpora to solve two different problems: to index the publications with the help of meta-keys and to identify the relevant publications in a large textual database. For that, we use factorial data analysis which allows us to find the minimal sets of relevant words that we call meta-keys and to free the bibliographical search from the problems of noise and silence. The performances of factorial correspondence analysis are sharply greater than classic search by logical equation.


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