| 书目名称 | Latent Semantic Mapping | | 副标题 | Principles and Appli | | 编辑 | Jerome R. Bellegarda | | 视频video | | | 丛书名称 | Synthesis Lectures on Speech and Audio Processing | | 图书封面 |  | | 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 | | doi | https://doi.org/10.1007/978-3-031-02556-3 | | isbn_softcover | 978-3-031-01428-4 | | isbn_ebook | 978-3-031-02556-3Series ISSN 1932-121X Series E-ISSN 1932-1678 | | issn_series | 1932-121X | | copyright | Springer Nature Switzerland AG 2007 |
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发表于 2025-3-21 18:14:37
