Latent Semantic Indexing for patent documents

Abstract

Abstract. Since the huge database of patent documents is continuously increasing, the issue of classifying, updating and retrieving patent documents turned into an acute necessity. Therefore we investigate the efficiency of apply-ing Latent Semantic Indexing, an automatic indexing method for information retrieval, to some classes of patent documents from the United States Patent Classification System. We present some experiments that provide the optimal number of dimensions for the Latent Semantic Space and we compare the per-formance of Latent Semantic Indexing (LSI) to the Vector Space Model (VSM) technique applied to real life text documents, namely patent documents. Though we do not strongly recommend the LSI as an improved alternative method to VSM, since the results are not significantly better.