2010 StatisticalSemaforEnhancDocClas

• (Farahat & Kamel, 2010) ⇒ Ahmed K. Farahat and Mohamed S. Kamel. (2010). “Statistical Semantics for Enhancing Document Clustering.” In: International Journal on Knowledge and Information Systems (KAIS), (28:2). doi:10.1007/s10115-010-0367-z

Subject Headings: Document clustering, Statistical semantics, Semantic similarity, Term–term correlations.

Cited By

• ~4 http://scholar.google.com/scholar?q=%22Statistical+Semantics+for+Enhancing+Document+Clustering%22+2010

Quotes

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 StatisticalSemaforEnhancDocClas	Ahmed K. Farahat Mohamed S. Kamel			Semantics for Enhancing Document Clustering		International Journal on Knowledge and Information System	http://dollar.biz.uiowa.edu/~nstreet/farahat10.pdf			2010