Latent semantic mapping (LSM) is a generalization of latent semantic research (LSA), a paradigm initially constructed to catch hidden notice styles in a textual content record corpus. In info retrieval, LSA allows retrieval at the foundation of conceptual content material, rather than purely matching phrases among queries and files. It operates below the idea that there's a few latent semantic constitution within the facts, that's partly obscured through the randomness of notice selection with admire to retrieval. Algebraic and/or statistical ideas are dropped at endure to estimate this constitution and cast off the obscuring noise. This leads to a parsimonious non-stop parameter description of phrases and records, which then replaces the unique parameterization in indexing and retrieval. This strategy shows 3 major features: Discrete entities (words and files) are mapped onto a continual vector house; this mapping is dependent upon international correlation styles; and dimensionality relief is a vital part of the method. Such particularly standard homes are valuable in quite a few diversified contexts, which motivates a broader interpretation of the underlying paradigm. the end result (LSM) is a data-driven framework for modeling significant worldwide relationships implicit in huge volumes of (not unavoidably textual) information. This monograph offers a normal review of the framework, and underscores the multifaceted advantages it may carry to a couple of difficulties in ordinary language realizing and spoken language processing. It concludes with a dialogue of the inherent tradeoffs linked to the technique, and a few views on its common applicability to data-driven info extraction.
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