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
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
Journal title :
World Patent Information