Contributions of Textual Data Analysis to Text Retrieval

The aim of this paper is to show how Textual Data Analysis techniques, developed in Europe under the influence of the Analyse Multidimen-sionelle des Données School, can improve performance of the LSI retrieval method. A first improvement can be obtained by properly considering the data contained in a lexical table. LSI is based on Euclidean distance, which is not adequate for frequency data. By using the chi-squared metric, on which Correspondence Analysis is based, significant improvements can be achieved. Further improvements can be obtained by considering external information such as keywords, authors, etc. Here an approach to text retrieval with external information based on PLS regression is shown. The suggested strategies are applied in text retrieval experiments on medical journal abstracts.