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by Tang Ting Kap. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 88-94). / Abstracts in English and Chinese. / List of Figures --- p.vi / List of Tables --- p.vii / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Information Overloading and Low Utilization --- p.2 / Chapter 1.2 --- Problem Needs To Solve --- p.3 / Chapter 1.3 --- Research Contributions --- p.4 / Chapter 1.3.1 --- Using Concept Space in Summarization --- p.5 / Chapter 1.3.2 --- New Extraction Method --- p.5 / Chapter 1.3.3 --- Experiments on New System --- p.6 / Chapter 1.4 --- Organization of This Thesis --- p.7 / Chapter 2. --- LITERATURE REVIEW --- p.8 / Chapter 2.1 --- Classical Approach --- p.8 / Chapter 2.1.1 --- Luhn's Algorithm --- p.9 / Chapter 2.1.2 --- Edumundson's Algorithm --- p.11 / Chapter 2.2 --- Statistical Approach --- p.15 / Chapter 2.3 --- Natural Language Processing Approach --- p.15 / Chapter 3. --- PROPOSED SUMMARIZATION APPROACH --- p.18 / Chapter 3.1 --- Direction of Summarization --- p.19 / Chapter 3.2 --- Overview of Summarization Algorithm --- p.20 / Chapter 3.2.1 --- Document Pre-processing --- p.21 / Chapter 3.2.2 --- Vector Space Model --- p.23 / Chapter 3.2.3 --- Sentence Extraction --- p.24 / Chapter 3.3 --- Evaluation Method --- p.25 / Chapter 3.3.1 --- "Recall, Precision and F-measure" --- p.25 / Chapter 3.4 --- Advantage of Concept Space Approach --- p.26 / Chapter 4. --- SYSTEM ARCHITECTURE --- p.27 / Chapter 4.1 --- Converge Process --- p.28 / Chapter 4.2 --- Diverge Process --- p.30 / Chapter 4.3 --- Backward Search --- p.31 / Chapter 5. --- CONVERGE PROCESS --- p.32 / Chapter 5.1 --- Document Merging --- p.32 / Chapter 5.2 --- Word Phrase Extraction --- p.34 / Chapter 5.3 --- Automatic Indexing --- p.34 / Chapter 5.4 --- Cluster Analysis --- p.35 / Chapter 5.5 --- Hopfield Net Classification --- p.37 / Chapter 6. --- DIVERGE PROCESS --- p.42 / Chapter 6.1 --- Concept Terms Refinement --- p.42 / Chapter 6.2 --- Sentence Selection --- p.43 / Chapter 6.3 --- Backward Searching --- p.46 / Chapter 7. --- EXPERIMENT AND RESEARCH FINDINGS --- p.48 / Chapter 7.1 --- System-generated Summary v.s. Source Documents --- p.52 / Chapter 7.1.1 --- Compression Ratio --- p.52 / Chapter 7.1.2 --- Information Loss --- p.54 / Chapter 7.2 --- System-generated Summary v.s. Human-generated Summary --- p.58 / Chapter 7.2.1 --- Background of EXTRACTOR --- p.59 / Chapter 7.2.2 --- Evaluation Method --- p.61 / Chapter 7.3 --- Evaluation of different System-generated Summaries by Human Experts --- p.63 / Chapter 8. --- CONCLUSIONS AND FUTURE RESEARCH --- p.68 / Chapter 8.1 --- Conclusions --- p.68 / Chapter 8.2 --- Future Work --- p.69 / Chapter A. --- EXTRACTOR SYSTEM FLOW AND TEN-STEP PROCEDURE --- p.71 / Chapter B. --- SUMMARY GENERATED BY MS WORD2000 --- p.75 / Chapter C. --- SUMMARY GENERATED BY EXTRACTOR SOFTWARE --- p.76 / Chapter D. --- SUMMARY GENERATED BY OUR SYSTEM --- p.77 / Chapter E. --- SYSTEM-GENERATED WORD PHRASES FROM TEST SAMPLE --- p.78 / Chapter F. --- WORD PHRASES IDENTIFIED BY SUBJECTS --- p.79 / Chapter G. --- SAMPLE OF QUESTIONNAIRE --- p.84 / Chapter H. --- RESULT OF QUESTIONNAIRE --- p.85 / Chapter I. --- EVALUATION FOR DIVERGE PROCESS --- p.86 / BIBLIOGRAPHY --- p.88

Chinese is very different from alphabetical languages such as English, as there are no delimiters between Chinese words. So Chinese segmentation is an important step for most Chinese natural language processing (NLP) tasks. We propose a tightness continuum for Chinese semantic units. The construction of the continuum is based on statistical informations. Based on this continuum, sequences can be dynamically segmented, and then that information can be exploited in a number of information retrieval tasks. In order to show that our tightness continuum is useful for NLP tasks, we propose two methods to exploit the tightness continuum within IR systems. The first method refines the result of a general Chinese word segmenter. The second method embeds the tightness value into IR score functions. Experimental results show that our tightness measure is reasonable and does improve the performance of IR systems.

Index size savings from three techniques are measured. The three techniques are: 1) eliminating common, low information words found in a "stop list" (such as: of, the, at, etc.), 2) truncating terms by eliminating word stems (such as: -s, -ed, -ing, etc.), and 3) simple data compression. Savings are measured on two moderately large collections of text. The index size savings that result from using the techniques individually and in combination are reported. The impact on query performance in terms of speed, recall and precision are estimated. / Graduation date: 1992

Thesis (M. Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Nov. 27, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.