Renovation Of Terms Adjustment And
Effective Model Combination Impact
On Information Retrieval Performance
Author(s)
A. Kasam & H.-C. Kwon
Abstract
Incorporating mechanism selection of a document, such as indexing and terms
weighting techniques, alleviates the data retrieval complexity and plays a
significant role in the performance of information retrieval and data mining
policy. The goals of information retrieval (IR) present the user automatically and
accurately with relevant data in answer to their queries in the form of documents.
To achieve the goals, an intelligent approach is to use the vector space model,
which models documents and queries as a number of indexed vectors in the
terms space. Unfortunately during document searching, the performance is
frequently interrupted by the appearance of numbers of uncertain fake documents
(such as stop words or absent terms etc). In reality, the same problems may be
solved in an intelligent way, so parallel research is needed. To meet the
requirements of the performance complexities by taking account not only
occurring terms but also absent terms in finding similarity of patterns among the
document and the query vectors could be included. In this paper, a balanced term
weighting scheme for a vector space model has been proposed as a novel method
to improve the retrieval performance. Additionally, a dimension compression
method has been included to minimize the computational cost. Under the
proposed scheme, a certain amount of Meta data have been tested and the results
strongly recommend to us that the proposed method is comparatively more
effective than any other information retrieval (IR) system. It could also be
applicable for academic and commercial purposes.
Keywords: information retrieval (IR), terms-weighting, balance term weighting
scheme (BTWS).
Keywords
information retrieval (IR), terms-weighting, balance term weighting
scheme (BTWS).
Related Book
Other papers in this volume
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