Latent Semantic Indexing for patent documents

Andreea Moldovan; Radu Boţ; Gert Wanka

International Journal of Applied Mathematics and Computer Science (2005)

  • Volume: 15, Issue: 4, page 551-560
  • ISSN: 1641-876X

Abstract

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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 applying Latent Semantic Indexing, an automatic indexing method of 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 (LSI) to the Vector Space Model (VSM) technique applied to real life text documents, namely, patent documents. However, we do not strongly recommend the LSI as an improved alternative method to the VSM, since the results are not significantly better.

How to cite

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Moldovan, Andreea, Boţ, Radu, and Wanka, Gert. "Latent Semantic Indexing for patent documents." International Journal of Applied Mathematics and Computer Science 15.4 (2005): 551-560. <http://eudml.org/doc/207766>.

@article{Moldovan2005,
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 applying Latent Semantic Indexing, an automatic indexing method of 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 (LSI) to the Vector Space Model (VSM) technique applied to real life text documents, namely, patent documents. However, we do not strongly recommend the LSI as an improved alternative method to the VSM, since the results are not significantly better.},
author = {Moldovan, Andreea, Boţ, Radu, Wanka, Gert},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {patent classification; Latent Semantic Indexing (LSI); Vector Space Model (VSM); Singular Value Decomposition (SVD); vector space model},
language = {eng},
number = {4},
pages = {551-560},
title = {Latent Semantic Indexing for patent documents},
url = {http://eudml.org/doc/207766},
volume = {15},
year = {2005},
}

TY - JOUR
AU - Moldovan, Andreea
AU - Boţ, Radu
AU - Wanka, Gert
TI - Latent Semantic Indexing for patent documents
JO - International Journal of Applied Mathematics and Computer Science
PY - 2005
VL - 15
IS - 4
SP - 551
EP - 560
AB - 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 applying Latent Semantic Indexing, an automatic indexing method of 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 (LSI) to the Vector Space Model (VSM) technique applied to real life text documents, namely, patent documents. However, we do not strongly recommend the LSI as an improved alternative method to the VSM, since the results are not significantly better.
LA - eng
KW - patent classification; Latent Semantic Indexing (LSI); Vector Space Model (VSM); Singular Value Decomposition (SVD); vector space model
UR - http://eudml.org/doc/207766
ER -

References

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