First Experiments with Relevant Documents Selection for Blind Relevance Feedback in Spoken Document Retrieval

Abstract

Abstract. This paper presents our first experiments aimed at the automatic selection of the relevant documents for the blind relevance feedback method in speech information retrieval. Usually the relevant documents are selected only by simply determining the first N documents to be relevant. We consider this approach to be insufficient and we would try in this paper to outline the possibilities of the dynamical selection of the relevant documents for each query depending on the content of the retrieved documents instead of just blindly defining the number of the relevant documents to be used for the blind relevance feedback in advance. We have performed initial experiments with the application of the score normalization techniques used in the speaker identification task, which was successfully used in the multi-label classification task for finding the "correct" topics of a newspaper article in the output of a generative classifier. The experiments have shown promising results, therefore they will be used to define the possibilities of the subsequent research in this area.