Generalized Latent Semantic Analysis for Document Representation
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Generalized Latent Semantic Analysis for Document Representation

Generalized Latent Semantic Analysis for Document Representation


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About the Book

Document representation has a large impact on the performance of document retrieval and clustering algorithms. Since content words can be combined into semantic classes there has been a considerable interest in low-dimensional term and document representations. We developed the Generalized Latent Semantic Analysis (GLSA) framework to extend the applicability of the idea of the latent semantic space analysis with spectral embedding. GLSA uses linguistically motivated measures of similarity between words and employs different dimensionality reduction techniques to preserve them. The main advantage of the GLSA representation for words and documents is the metric in the resulting low-dimensional space. Cosine and inner product similarities in the GLSA space are linguistically motivated so that GLSA improves the performance of many approaches which are based on pair-wise similarities. Most language modelling and dimensionality reduction approaches are applied to the full vocabulary. However, semantics and syntax provide a theoretical justification for treating subsets of the vocabulary differently. Moreover, the theoretical justification of GLSA suggests that it is most appropriate to apply it to a subset of the vocabulary comprised of content words. Therefore, we proposed to use GLSA with a hybrid approach to document indexing. Our goal was to keep the notion of latent semantic concepts and at the same time to preserve the specifics of the document collection. We evaluated the GLSA representations on a number of tasks. We evaluated the GLSA representation for words on the synonymy test and the term pairs test; we evaluated the GLSA indexing and hybrid document indexing with GLSA on document classification, document clustering and text segmentation tasks. Our experiments showed that GLSA improves the results of the similarity based document classification and clustering algorithms and text segmentation algorithms. GLSA significantly outperformed the baseline and LSA on all applications that we used in our experiments.


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Product Details
  • ISBN-13: 9781243453136
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 246 mm
  • Weight: 299 gr
  • ISBN-10: 1243453133
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Spine Width: 9 mm
  • Width: 189 mm


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Generalized Latent Semantic Analysis for Document Representation
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Generalized Latent Semantic Analysis for Document Representation
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