We tested the hypothesis that top management team¡¦s effectiveness and Chaxu Geju have an positive effect relationship. Our analysis was base on data from 650 schools , including 120 pairs of team leaders and their team's members .To survey whether the leader's Chaxu Geju which is Guanxi , loyalty and talent has positive effect on team's effectiveness. The first result shows, Team leader will divide team members into insiders and outsiders according to Chaxu Geju . Finally, indicated leader¡¦s criterion of categorization didn¡¦t significant affect the top management team¡¦s effectiveness. The two group insiders and outsiders are the same .

Text categorization to automatically assign documents into the appropriate pre-defined category or categories is essential to facilitating the retrieval of desired documents efficiently and effectively from a huge text depository, e.g., the world-wide web. Most techniques, however, suffer from the feature selection problem and the vocabulary mismatch problem. A few research works have addressed on text categorization via text summarization to reduce the size of documents, and consequently the number of features to consider, while some proposed using latent semantic indexing (LSI) to reveal the true meaning of a term via its association with other terms. Few works, however, have studied the joint effect of text summarization and the semantic dimension reduction technique in the literature. The objective of this research is thus to propose a practical approach, SBDR to deal with the above difficulties in text categorization tasks. Two experiments are conducted to validate our proposed approach. In the first experiment, the results show that text summarization does improve the performance in categorization. In addition, to construct important sentences, the association terms of both noun-noun and noun-verb pairs should be considered. Results of the second experiment indicate slight better performance with the approach of adopting LSI exclusively (i.e. no summarization) than that with SBDR (i.e. with summarization). Nonetheless, the minor accuracy reduction can be largely compensated for the computational time saved using LSI with text summarized. The feasibility of the SBDR approach is thus justified.

As text repositories grow in number and size and global connectivity improves, the amount of online information in the form of free-format text is growing extremely rapidly. In many large organizations, huge volumes of textual information are created and maintained, and there is a pressing need to support efficient and effective information retrieval, filtering, and management. Text categorization is essential to the efficient management and retrieval of documents. Past research on text categorization mainly focused on developing or adopting statistical classification or inductive learning methods for automatically discovering text categorization patterns from a training set of manually categorized documents. However, as documents accumulate, the pre-defined categories may not capture the characteristics of the documents. In this study, we proposed a mining-based category evolution (MiCE) technique to adjust the categories based on the existing categories and their associated documents. According to the empirical evaluation results, the proposed technique, MiCE, was more effective than the discovery-based category management approach, insensitive to the quality of original categories, and capable of improving classification accuracy.

Thesis (M.A.)--Miami University, Dept. of Speech Pathology and Audiology, 2002. / Title from first page of PDF document. Document formatted into pages; contains ix, 86 p. Includes bibliographical references (p. 69-75).

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Typescript (photocopy) / Bibliography: leaves 82-88. / Microfiche. / ix, 88 leaves, bound ill. 28 cm

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