By N. C.) Computational Information Retrieval Conference (2000 Raleigh, Michael W. Berry, Michael W. Berry, Society for Industrial and Applied Mathematics

ISBN-10: 0898715008

ISBN-13: 9780898715002

This quantity includes chosen papers that concentrate on using linear algebra, computational records, and desktop technology within the improvement of algorithms and software program structures for textual content retrieval. specialists in details modeling and retrieval proportion their views at the layout of scalable yet detailed textual content retrieval platforms, revealing the various demanding situations and hindrances that mathematical and statistical versions needs to conquer to be doable for computerized textual content processing. This very priceless court cases is a superb significant other for classes in details retrieval, utilized linear algebra, and utilized information.

Computational details Retrieval presents history fabric on vector area versions for textual content retrieval that utilized mathematicians, statisticians, and machine scientists is probably not accustomed to. For graduate scholars in those components, a number of learn questions in details modeling are uncovered. furthermore, a number of case reviews about the efficacy of the preferred Latent Semantic research (or Indexing) process are supplied.

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A new term weighting factor for queries is given in Section 4. In Section 5, we show some preliminary experimental results. Finally, in Section 6, we show how to extend this method of information retrieval to text classification. 2 Subspace representation In TRUST, we obtain our t x d working matrix A by normalizing the columns of matrix D to have unit sum, stabilizing the variance of terms via a nonlinear function, and then centering with respect to the mean vector of the columns. Hence we abbreviate our current preprocessing as A — f(D)—ceT in which c is the mean vector and e is a d-vector all of whose components are 1, so that the average of the columns of A is now 0.

H. GOLUB, A Rank-Reduction Formula and its Applications to Matrix Factorizations, SIAM Review 37:512-530, 1995. E. E. FUNDERLIC, The Rank of a Difference of Matrices and Associated Generalized Inverses, Linear Algebra Appl. 24:185-215, 1979. T. W. K. LANDAUER, AND R. HARSHMAN, Indexing by Latent Semantic Analysis, J. of the Society for Information Science 41:391-407, 1990. [10] I. S. DHILLON, Concept Decompositions for Large Sparse Text Data using Clustering, IBM Research Report, RJ 10147, 1999.

6 Text Classification In this section we will show how the query-by-keyword method can be extended to text classification. Text classification, also known as text categorization, is a text mining application which classifies or categorizes a new document into one or more of a set of pre-defined classes or categories based on a set of training examples that have already been classified. All classification applications require that • there is a set of pre-defined classes, and • there is training data consisting of samples of data for each class.

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Computational information retrieval by N. C.) Computational Information Retrieval Conference (2000 Raleigh, Michael W. Berry, Michael W. Berry, Society for Industrial and Applied Mathematics


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