Title of article
Advanced document retrieval techniques for patent research Original Research Article
Author/Authors
James F. Ryley، نويسنده , , Jeff Saffer، نويسنده , , Andy Gibbs، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2008
Pages
6
From page
238
To page
243
Abstract
Latent semantic indexing (LSI) can be used in patent searching to overcome drawbacks of Boolean searching and to give more accurate retrieval. LSI combines the vector space model (VSM) of document retrieval with single value decomposition (SVD), using linear algebra techniques to uncover word relationships in the text. Results can be enhanced by using text clustering and tailoring SVD parameters to the specific corpus, in this case, patents, and by employing techniques to address ambiguities in language.
Keywords
LSI , Vector space model , VSM , Single value decomposition , text mining , Clustering , Patents , SVD , Latent semantic indexing
Journal title
World Patent Information
Serial Year
2008
Journal title
World Patent Information
Record number
1230429
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