Choosing a proper metrics for textual analysis

The definition of good strategies for Text Retrieval has become in recent years more and more important. In literature, statistical approaches are frequently mentioned (perhaps, more than used), in an “exploratory data analysis state of mind” (e.g. Greiff, 1998). One crucial point consists in understanding andmeasuring the specificity of each term in order to obtain a mean for measuring the relevance of a document with respect to a query. Many different solutions have been proposed. In Vector Space Models (Salton, 1983) the weighting step considers the term and collection frequency and the length normalization factor. In the canonical for m of Latent Semantic Indexing (Deerwester et al., 1990) weights are given by raw term frequencies. In Latent Semantic Correspondence Indexing (Balbi & Di Meglio, 2004) a weight related to chi-square metrics has been suggested. Aim of this paper is to propose, having in mind the so-called “general analysis in any distances and any criteria”, a distance and a criterion related to terms relevance and specificity, in order to reconsider the weighting systemin a Lexical Correspondence Analysis scheme