Moldovan, Andreea ; Bot, Radu Ioan ; Wanka, Gert : Latent Semantic Indexing for Patent Documents

Author(s):

Moldovan, Andreea
Bot, Radu Ioan
Wanka, Gert

Title:

Latent Semantic Indexing for Patent Documents

Electronic source:

application/pdf

Preprint series:

Technische Universität Chemnitz, Fakultät für Mathematik (Germany). Preprint 12, 2004

Mathematics Subject Classification:

62H30			[ Classification and discrimination; cluster analysis ]
15A18			[ Eigenvalues, singular values, and eigenvectors ]
68P20			[ Information storage and retrieval ]

Abstract:

Since the huge amount of patent documents database is continuously increasing, the issue of classifying, updating and retrieving patent documents turned into an acute necessity. Therefore we investigate the efficiency of applying 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 performance of Latent Semantic Indexing to the Vector Space Model technique applied to real life text documents, namely patent documents.

Keywords:

Latent Semantic Indexing (LSI); Singular Value Decomposition(SVD); Vector Space Model (VSM); Patent Classification

Language:

English

Publication time:

8 / 2004