The vector space model (VSM) of information retrieval suf- fers in two areas, it does not utilise term positions and it treats every term as being independent. We examine two information retrieval meth- ods based on the simple vector space model. The first uses the query term position flow within the documents to calculate the document score, the second includes related terms in the query by making use of term corre- lations. Both of these methods show significant improvements over the VSM precision while keeping the query time to speeds similar to those of the VSM.

Current information retrieval methods either ignore the term positions or deal with exact term positions; the former can be seen as coarse document resolution, the latter as fine document reso- lution. We propose a new spectral-based information retrieval method that is able to utilize many different levels of document resolution by examining the term patterns that occur in the documents. To do this, we take advantage of the multiresolution analysis properties of the wavelet transform. We show that we are able to achieve higher precision when compared to vector space and proximity retrieval methods, while producing fast query times and using a compact index.

Current information retrieval methods either ignore the term positions or deal with exact term positions; the former can be seen as coarse document resolution, the latter as fine document reso- lution. We propose a new spectral-based information retrieval method that is able to utilize many different levels of document resolution by examining the term patterns that occur in the documents. To do this, we take advantage of the multiresolution analysis properties of the wavelet transform. We show that we are able to achieve higher precision when compared to vector space and proximity retrieval methods, while producing fast query times and using a compact index.