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Titlebook: Latent Semantic Mapping; Principles and Appli Jerome R. Bellegarda Book 2007 Springer Nature Switzerland AG 2007

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書目名稱Latent Semantic Mapping
副標(biāo)題Principles and Appli
編輯Jerome R. Bellegarda
視頻videohttp://file.papertrans.cn/582/581800/581800.mp4
叢書名稱Synthesis Lectures on Speech and Audio Processing
圖書封面Titlebook: Latent Semantic Mapping; Principles and Appli Jerome R. Bellegarda Book 2007 Springer Nature Switzerland AG 2007
描述Latent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus.In information retrieval, LSA 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 approach exhibits three main characteristics:-Discrete entities (words and documents) are mapped onto a continuous vector space;-This mapping is determined by global correlation patterns; and-Dimensionality reduction is an integral part of the process.Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradig
出版日期Book 2007
版次1
doihttps://doi.org/10.1007/978-3-031-02556-3
isbn_softcover978-3-031-01428-4
isbn_ebook978-3-031-02556-3Series ISSN 1932-121X Series E-ISSN 1932-1678
issn_series 1932-121X
copyrightSpringer Nature Switzerland AG 2007
The information of publication is updating

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Junk E-Mail Filteringcomes closest to its ordinary meaning.. With units and compositions so defined, the LSM properties can be exploited in several areas of natural language processing. One of the conceptually simplest applications is junk e-mail filtering. To see how the LSM framework can be leveraged in this domain, l
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Pronunciation Modelingthe beginning of this monograph, the fairly generic nature of the LSM properties makes the framework amenable to a variety of tasks beyond this original setup. This Chapter explores a recent foray into one such application, which has to do with modeling the pronunciation of words in the language.
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TTS Unit Selectionchapter explores a recent foray into a even more general application, in the context of concatenative TTS synthesis. This exemplifies how LSM can also be leveraged in generic signal processing problems.
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the trajectory of a weather event can thus be a painstaking process taking years and involving real detective work. Missing pieces of a climate puzzle can come from very far afield, often in unlikely places. In this book, a series of case studies examine specific regions across North America, using
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