PROBABILISTIC AUTOMATIC INDEXING BY LEARNING FROM HUMAN INDEXERS

Abstract

A probabilistic model previously used in relevance feedback is adapted for use in automatic indexing of documents (in the sense of imitating human indexers). The model fits with previous work in this area (the ‘adhesion coefficient’ method), in effect merely suggesting a different way of arriving at the adhesion coefficients. Methods for the application of the model are proposed. The independence assumptions used in the model are interpreted, and the possibility of a dependence model is discussed.