Statistical semantics for enhancing document clustering View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-08

AUTHORS

Ahmed K. Farahat, Mohamed S. Kamel

ABSTRACT

Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic similarity. This paper proposes new models for document representation that capture semantic similarity between documents based on measures of correlations between their terms. The paper uses the proposed models to enhance the effectiveness of different algorithms for document clustering. The proposed representation models define a corpus-specific semantic similarity by estimating measures of term–term correlations from the documents to be clustered. The corpus of documents accordingly defines a context in which semantic similarity is calculated. Experiments have been conducted on thirteen benchmark data sets to empirically evaluate the effectiveness of the proposed models and compare them to VSM and other well-known models for capturing semantic similarity. More... »

PAGES

365-393

References to SciGraph publications

  • 1999-10. Learning the parts of objects by non-negative matrix factorization in NATURE
  • 2007-12. A tutorial on spectral clustering in STATISTICS AND COMPUTING
  • 2004-06. Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering in MACHINE LEARNING
  • 2009. Clustering Documents Using a Wikipedia-Based Concept Representation in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2003. Comparing Clusterings by the Variation of Information in LEARNING THEORY AND KERNEL MACHINES
  • 2004. Feature Selection and Document Clustering in SURVEY OF TEXT MINING
  • 2005-03. Hierarchical Clustering Algorithms for Document Datasets in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2010-10. Knowledge-based vector space model for text clustering in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2002-03. Latent Semantic Kernels in JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
  • 2001-01. Concept Decompositions for Large Sparse Text Data Using Clustering in MACHINE LEARNING
  • 2009-06. Using Wikipedia knowledge to improve text classification in KNOWLEDGE AND INFORMATION SYSTEMS
  • 1997. Kernel principal component analysis in ARTIFICIAL NEURAL NETWORKS — ICANN'97
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10115-010-0367-z

    DOI

    http://dx.doi.org/10.1007/s10115-010-0367-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1006383366


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