By Jerome R. Bellegarda, B.h. Juang (Series Editor)
In information retrieval, Latent Semantic Mapping enables retrieval on the basis of conceptual content instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring "noise." This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This monograph gives a general overview of the framework and underscores the multi-faceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent trade-offs associated with the approach and some perspectives on its general applicability to unsupervised information extraction.
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