@misc{Moldovan_Andreea_Latent,
 author={Moldovan, Andreea and Bot, Radu Ioan and Wanka, Gert},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={Since the huge database of patent documents is continuously increasing, the issue of classifying, updating and retrievingpatent documents turned into an acute necessity. Therefore, we investigate the efficiency of applying Latent SemanticIndexing, an automatic indexing method of information retrieval, to some classes of patent documents from the UnitedStates Patent Classification System. We present some experiments that provide the optimal number of dimensions for theLatent Semantic Space and we compare the performance of Latent Semantic Indexing (LSI) to the Vector Space Model(VSM) technique applied to real life text documents, namely, patent documents. However, we do not strongly recommendthe LSI as an improved alternative method to the VSM, since the results are not significantly better.},
 type={artykuł},
 title={Latent semantic indexing for patent documents},
 keywords={Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD), Vector Space Model (VSM), patent classification},
}