A novel supervised text classifier from a small training set

It is well known that one way to improve the accuracy of a text retrieval system is to expand the original query with additional knowledge coded through topic-related terms. In the case of an interactive environment, the expansion, which is usually represented as a list of words, is extracted from documents whose relevance is known thanks to the feedback of the user. In this paper we argue that the accuracy of a text retrieval system can be improved if we employ a query expansion method based on a mixed Graph of Terms representation instead of a method based on a simple list of words. The graph, that is composed of a directed and an undirected subgraph, can be automatically extracted from a small set of only relevant documents (namely the user feedback) using a method for term extraction based on the probabilistic Topic Model. The evaluation of the proposed method has been carried out by performing a comparison with two less complex structures: one represented as a set of pairs of words and another that is a simple list of words.