Latent variable models and factors analysis
Latent variable models and factors analysis
Automatic structuring and retrieval of large text files
Communications of the ACM
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using Linear Algebra for Intelligent Information Retrieval
Using Linear Algebra for Intelligent Information Retrieval
A probabilistic model for Latent Semantic Indexing: Research Articles
Journal of the American Society for Information Science and Technology
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Model-averaged latent semantic indexing
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Unified linear subspace approach to semantic analysis
Journal of the American Society for Information Science and Technology
Understanding latent semantic indexing: A topological structure analysis using Q-analysis
Journal of the American Society for Information Science and Technology
Principal components for automatic term hierarchy building
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Discovering a term taxonomy from term similarities using principal component analysis
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
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We seek insight into Latent Semantic Indexing by establishing a method to identify the optimal number of factors in the reduced matrix for representing a keyword. This method is demonstrated empirically by duplicating all documents containing a term t, and inserting new documents in the database that replace t with t'. By examining the number of times term t is identified for a search on term t' (precision) using differing ranges of dimensions, we find that lower ranked dimensions identify related terms and higher-ranked dimensions discriminate between the synonyms.