Latent semantic mapping: dimensionality reduction via globally optimal continuous parameter modeling

Originally formulated in the context of information retrieval, latent semantic analysis exhibits three main characteristics: (i) discrete entities (namely words and documents) are mapped onto a continuous vector space; (ii) this mapping is determined by global correlation patterns; and (iii) dimensionality reduction is an integral part of the process. Such fairly generic properties may be advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome is latent semantic mapping, a data-driven framework for modeling global relationships implicit in large volumes of (not necessarily textual) data. This paper 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