document: CVS : a Correlation-Verification based Smoothing technique on information retrieval and term clustering As information volume in enterprise systems and in the Web grows rapidly , how to accurately retrieve information is an important research area . Several corpus based smoothing techniques have been proposed to address the data sparsity and synonym problems faced by information retrieval systems . Such smoothing techniques are often unable to discover and utilize the correlations among terms . We propose CVS , a Correlation-Verification based Smoothing method , that considers co-occurrence information in smoothing . Strongly correlated terms in a document are identified by their co-occurrence frequencies in the document . To avoid missing correlated terms with low co-occurrence frequencies but specific to the theme of the document , the joint distributions of terms in the document are compared with those in the corpus for statistical significance . A common approach to apply corpus based smoothing techniques to information retrieval is by refining the vector representations of documents . This paper investigates the effects of corpus based smoothing on information retrieval by query expansion using term clusters generated from a term clustering process . The results can also be viewed in light of the effects of smoothing on clustering . Empirical studies show that our approach outperforms previous corpus based smoothing techniques . It improves retrieval effectiveness by 14.6 % . The results demonstrate that corpus based smoothing can be used for query expansion by term clustering .
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