Browsing by Author "Jung, Yunjae"

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    A Balanced Term-Weighting Scheme for Effective Document Matching
    (2001-02-07) Jung, Yunjae; Park, Haesun; Du, Ding-Zhu
    A new weighting scheme for vector space model is presented to improve retrieval performance for an information retrieval system. In addition, a dimension compression method is introduced to reduce the computational cost of the weighting approach. The main idea of this approach is to consider not only occurrence terms but also absent terms in finding similarity patterns among document and query vectors. With a basic information retrieval development system which we are now developing, we evaluate the effect of the balanced weighting scheme and compare it with various combinations of weighting schemes in terms of retrieval performance. The experimental results show that the proposed scheme produces similar recall-precision results to the cosine measure, but more importantly enhances retrieval effectiveness. Since the scheme is based on the cosine measure, it is certain that it has insensitivity to weight variance. The results have convincingly illustrated that the new approach is effective and applicable.
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    An Effective Term-Weighting Scheme for Information Retrieval
    (2000-02-07) Jung, Yunjae; Park, Haesun; Du, Ding-Zhu
    The retrieval performance of the information retrieval systems is largely dependent on similarity measures. Furthermore, a term-weighting scheme plays an important role for the similarity measure. In this paper, we investigate existing weighting approaches and similarity measures to propose a new term-weighting scheme supporting the cosine similarity measure to retrieve relevant documents effectively on the basis of vector space model. The new weighting technique considers not only occurrence terms but also absence terms in finding similarity among document vector representations. The consideration of negatively weighted terms resolves the masking by zero problem of the inner product operation. According to the experimental results, the balanced term-weighting scheme performs more effectively especially when the vector space of data collection is relatively dense. Consequently, the balanced weighting scheme promises significant contribution to the relevance effectiveness for the information retrieval systems.
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    Optimal Consecutive-k-out-of-(2k+1): G Cycle
    (2000-10-02) Du, Ding-Zhu; Hwang, Frank K.; Jung, Yunjae; Ngo, Hung Q.
    We present a complete proof for the invariant optimal assignment for consecutive-k-out-of-(2k+1): G Cycle, which was proposed by Zuo and Kao in 1990 with an incomplete proof, pointed out recently by Jalali, Hawkes, Cui, and Hwang.