Title :
Meta latent semantic analysis
Author :
Simina, Marin ; Barbu, Costin
Author_Institution :
Dept. of CIS, Loyola Univ., New Orleans, LA, USA
Abstract :
Meta latent semantic analysis (MLSA) is a novel approach to automated document analysis and indexing which relies on symbolic ontologies to further enhance the traditional probabilistic latent semantic analysis (LSA) of documents. While LSA is able to discover clusters of related terms and documents in a given collection of documents, the proposed MLSA is able to meta-cluster such clusters by taking into account existing symbolic ontologies relevant for the analyzed collections of documents. Such an approach can be successfully used to improve the performance of fast LSA by random projection.
Keywords :
document handling; indexing; ontologies (artificial intelligence); semantic networks; automated document analysis; meta latent semantic analysis; meta-cluster; random projection; symbolic ontologies; traditional probabilistic latent semantic analysis; Computational Intelligence Society; Indexing; Information analysis; Information retrieval; Ontologies; Sampling methods; Singular value decomposition; Text analysis; User interfaces;
Conference_Titel :
Systems, Man and Cybernetics, 2004 IEEE International Conference on
Print_ISBN :
0-7803-8566-7
DOI :
10.1109/ICSMC.2004.1400922
