Ip Chun Wah Timmy. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 113-120). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Topic Detection and Tracking --- p.2 / Chapter 1.1.1 --- What is a Topic? --- p.3 / Chapter 1.1.2 --- What is Topic Tracking? --- p.4 / Chapter 1.2 --- Research Contributions --- p.4 / Chapter 1.2.1 --- Named Entity Tagging --- p.5 / Chapter 1.2.2 --- Handling Unknown Words --- p.6 / Chapter 1.2.3 --- Named-Entity Approach in Topic Tracking --- p.7 / Chapter 1.3 --- Organization of Thesis --- p.7 / Chapter 2 --- Background --- p.9 / Chapter 2.1 --- Previous Developments in Topic Tracking --- p.10 / Chapter 2.1.1 --- BBN's Tracking System --- p.10 / Chapter 2.1.2 --- CMU's Tracking System --- p.11 / Chapter 2.1.3 --- Dragon's Tracking System --- p.12 / Chapter 2.1.4 --- UPenn's Tracking System --- p.13 / Chapter 2.2 --- Topic Tracking in Chinese --- p.13 / Chapter 2.3 --- Part-of-Speech Tagging --- p.15 / Chapter 2.3.1 --- A Brief Overview of POS Tagging --- p.15 / Chapter 2.3.2 --- Transformation-based Error-Driven Learning --- p.18 / Chapter 2.4 --- Unknown Word Identification --- p.20 / Chapter 2.4.1 --- Rule-based approaches --- p.21 / Chapter 2.4.2 --- Statistical approaches --- p.23 / Chapter 2.4.3 --- Hybrid approaches --- p.24 / Chapter 2.5 --- Information Retrieval Models --- p.25 / Chapter 2.5.1 --- Vector-Space Model --- p.26 / Chapter 2.5.2 --- Probabilistic Model --- p.27 / Chapter 2.6 --- Chapter Summary --- p.28 / Chapter 3 --- System Overview --- p.29 / Chapter 3.1 --- Segmenter --- p.30 / Chapter 3.2 --- TEL Tagger --- p.31 / Chapter 3.3 --- Unknown Words Identifier --- p.32 / Chapter 3.4 --- Topic Tracker --- p.33 / Chapter 3.5 --- Chapter Summary --- p.34 / Chapter 4 --- Named Entity Tagging --- p.36 / Chapter 4.1 --- Experimental Data --- p.37 / Chapter 4.2 --- Transformational Tagging --- p.41 / Chapter 4.2.1 --- Notations --- p.41 / Chapter 4.2.2 --- Corpus Utilization --- p.42 / Chapter 4.2.3 --- Lexical Rules --- p.42 / Chapter 4.2.4 --- Contextual Rules --- p.47 / Chapter 4.3 --- Experiment and Result --- p.49 / Chapter 4.3.1 --- Lexical Tag Initialization --- p.50 / Chapter 4.3.2 --- Contribution of Lexical and Contextual Rules --- p.52 / Chapter 4.3.3 --- Performance on Unknown Words --- p.56 / Chapter 4.3.4 --- A Possible Benchmark --- p.57 / Chapter 4.3.5 --- Comparison between TEL Approach and the Stochas- tic Approach --- p.58 / Chapter 4.4 --- Chapter Summary --- p.59 / Chapter 5 --- Handling Unknown Words in Topic Tracking --- p.62 / Chapter 5.1 --- Overview --- p.63 / Chapter 5.2 --- Person Names --- p.64 / Chapter 5.2.1 --- Forming possible named entities from OOV by group- ing n-grams --- p.66 / Chapter 5.2.2 --- Overlapping --- p.69 / Chapter 5.3 --- Organization Names --- p.71 / Chapter 5.4 --- Location Names --- p.73 / Chapter 5.5 --- Dates and Times --- p.74 / Chapter 5.6 --- Chapter Summary --- p.75 / Chapter 6 --- Topic Tracking in Chinese --- p.77 / Chapter 6.1 --- Introduction of Topic Tracking --- p.78 / Chapter 6.2 --- Experimental Data --- p.79 / Chapter 6.3 --- Evaluation Methodology --- p.81 / Chapter 6.3.1 --- Cost Function --- p.82 / Chapter 6.3.2 --- DET Curve --- p.83 / Chapter 6.4 --- The Named Entity Approach --- p.85 / Chapter 6.4.1 --- Designing the Named Entities Set for Topic Tracking --- p.85 / Chapter 6.4.2 --- Feature Selection --- p.86 / Chapter 6.4.3 --- Integrated with Vector-Space Model --- p.87 / Chapter 6.5 --- Experimental Results and Analysis --- p.91 / Chapter 6.5.1 --- Notations --- p.92 / Chapter 6.5.2 --- Stopword Elimination --- p.92 / Chapter 6.5.3 --- TEL Tagging --- p.95 / Chapter 6.5.4 --- Unknown Word Identifier --- p.100 / Chapter 6.5.5 --- Error Analysis --- p.106 / Chapter 6.6 --- Chapter Summary --- p.108 / Chapter 7 --- Conclusions and Future Work --- p.110 / Chapter 7.1 --- Conclusions --- p.110 / Chapter 7.2 --- Future Work --- p.111 / Bibliography --- p.113 / Chapter A --- The POS Tags --- p.121 / Chapter B --- Surnames and transliterated characters --- p.123 / Chapter C --- Stopword List for Person Name --- p.126 / Chapter D --- Organization suffixes --- p.127 / Chapter E --- Location suffixes --- p.128 / Chapter F --- Examples of Feature Table (Train set with condition D410) --- p.129

Wong Kam Lai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 92-98). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Our Contributions --- p.5 / Chapter 1.2 --- Organization of this Thesis --- p.5 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Dragon Systems --- p.7 / Chapter 2.2 --- Carnegie Mellon University (CMU) --- p.9 / Chapter 2.3 --- University of Massachusetts (UMass) --- p.10 / Chapter 2.4 --- IBM T.J. Watson Research Center --- p.11 / Chapter 2.5 --- BBN Technologies --- p.12 / Chapter 2.6 --- National Taiwan University (NTU) --- p.13 / Chapter 2.7 --- Drawbacks of Existing Approaches --- p.14 / Chapter 3 --- Overview of Proposed Approach --- p.15 / Chapter 3.1 --- News Source --- p.15 / Chapter 3.2 --- Story Preprocessing --- p.18 / Chapter 3.3 --- Concept Term Generation --- p.20 / Chapter 3.4 --- Named Entity Extraction --- p.21 / Chapter 3.5 --- Gross Translation of Chinese to English --- p.21 / Chapter 3.6 --- Topic Detection method --- p.22 / Chapter 3.6.1 --- Deferral Period --- p.22 / Chapter 3.6.2 --- Detection Approach --- p.23 / Chapter 4 --- Concept Term Model --- p.25 / Chapter 4.1 --- Background of Contextual Analysis --- p.25 / Chapter 4.2 --- Concept Term Generation --- p.28 / Chapter 4.2.1 --- Concept Generation Algorithm --- p.28 / Chapter 4.2.2 --- Concept Term Representation for Detection --- p.33 / Chapter 5 --- Topic Detection Model --- p.35 / Chapter 5.1 --- Text Representation and Term Weights --- p.35 / Chapter 5.1.1 --- Story Representation --- p.35 / Chapter 5.1.2 --- Topic Representation --- p.43 / Chapter 5.1.3 --- Similarity Score --- p.43 / Chapter 5.1.4 --- Time adjustment scheme --- p.46 / Chapter 5.2 --- Gross Translation Method --- p.48 / Chapter 5.3 --- The Detection System --- p.50 / Chapter 5.3.1 --- Detection Requirement --- p.50 / Chapter 5.3.2 --- The Top Level Model --- p.52 / Chapter 5.4 --- The Clustering Algorithm --- p.55 / Chapter 5.4.1 --- Similarity Calculation --- p.55 / Chapter 5.4.2 --- Grouping Related Elements --- p.56 / Chapter 5.4.3 --- Topic Identification --- p.60 / Chapter 6 --- Experimental Results and Analysis --- p.63 / Chapter 6.1 --- Evaluation Model --- p.63 / Chapter 6.1.1 --- Evaluation Methodology --- p.64 / Chapter 6.2 --- Experiments on the effects of tuning the parameter --- p.68 / Chapter 6.2.1 --- Experiment Setup --- p.68 / Chapter 6.2.2 --- Results and Analysis --- p.69 / Chapter 6.3 --- Experiments on the effects of named entities and concept terms --- p.74 / Chapter 6.3.1 --- Experiment Setup --- p.74 / Chapter 6.3.2 --- Results and Analysis --- p.75 / Chapter 6.4 --- Experiments on the effect of using time adjustment --- p.77 / Chapter 6.4.1 --- Experiment Setup --- p.77 / Chapter 6.4.2 --- Results and Analysis --- p.79 / Chapter 6.5 --- Experiments on mono-lingual detection --- p.80 / Chapter 6.5.1 --- Experiment Setup --- p.80 / Chapter 6.5.2 --- Results and Analysis --- p.80 / Chapter 7 --- Conclusions and Future Work --- p.83 / Chapter 7.1 --- Conclusions --- p.83 / Chapter 7.2 --- Future Work --- p.85 / Chapter A --- List of Topics annotated for TDT3 Corpus --- p.86 / Chapter B --- Matching evaluation topics to hypothesized topics --- p.90 / Bibliography --- p.92

Hui Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 115-120). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Topic Detection Problem --- p.2 / Chapter 1.1.1 --- What is a Topic? --- p.2 / Chapter 1.1.2 --- Topic Detection --- p.3 / Chapter 1.2 --- Our Contributions --- p.5 / Chapter 1.2.1 --- Thesis Organization --- p.6 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Dragon Systems --- p.7 / Chapter 2.2 --- University of Massachusetts (UMass) --- p.9 / Chapter 2.3 --- Carnegie Mellon University (CMU) --- p.10 / Chapter 2.4 --- BBN Technologies --- p.11 / Chapter 2.5 --- IBM T. J. Watson Research Center --- p.12 / Chapter 2.6 --- National Taiwan University (NTU) --- p.13 / Chapter 2.7 --- Drawbacks of Existing Approaches --- p.14 / Chapter 3 --- System Overview --- p.16 / Chapter 3.1 --- News Sources --- p.17 / Chapter 3.2 --- Story Preprocessing --- p.21 / Chapter 3.3 --- Named Entity Extraction --- p.22 / Chapter 3.4 --- Gross Translation --- p.22 / Chapter 3.5 --- Unsupervised Learning Module --- p.24 / Chapter 4 --- Term Extraction and Story Representation --- p.27 / Chapter 4.1 --- IBM Intelligent Miner For Text --- p.28 / Chapter 4.2 --- Transformation-based Error-driven Learning --- p.31 / Chapter 4.2.1 --- Learning Stage --- p.32 / Chapter 4.2.2 --- Design of New Tags --- p.33 / Chapter 4.2.3 --- Lexical Rules Learning --- p.35 / Chapter 4.2.4 --- Contextual Rules Learning --- p.39 / Chapter 4.3 --- Extracting Named Entities Using Learned Rules --- p.42 / Chapter 4.4 --- Story Representation --- p.46 / Chapter 4.4.1 --- Basic Representation --- p.46 / Chapter 4.4.2 --- Enhanced Representation --- p.47 / Chapter 5 --- Gross Translation --- p.52 / Chapter 5.1 --- Basic Translation --- p.52 / Chapter 5.2 --- Enhanced Translation --- p.60 / Chapter 5.2.1 --- Parallel Corpus Alignment Approach --- p.60 / Chapter 5.2.2 --- Enhanced Translation Approach --- p.62 / Chapter 6 --- Unsupervised Learning Module --- p.68 / Chapter 6.1 --- Overview of the Discovery Algorithm --- p.68 / Chapter 6.2 --- Topic Representation --- p.70 / Chapter 6.3 --- Similarity Calculation --- p.72 / Chapter 6.3.1 --- Similarity Score Calculation --- p.72 / Chapter 6.3.2 --- Time Adjustment Scheme --- p.74 / Chapter 6.3.3 --- Language Normalization Scheme --- p.75 / Chapter 6.4 --- Related Elements Combination --- p.78 / Chapter 7 --- Experimental Results and Analysis --- p.84 / Chapter 7.1 --- TDT corpora --- p.84 / Chapter 7.2 --- Evaluation Methodology --- p.85 / Chapter 7.3 --- Experimental Results on Various Parameter Settings --- p.88 / Chapter 7.4 --- Experiments Results on Various Named Entity Extraction Ap- proaches --- p.89 / Chapter 7.5 --- Experiments Results on Various Story Representation Approaches --- p.100 / Chapter 7.6 --- Experiments Results on Various Translation Approaches --- p.104 / Chapter 7.7 --- Experiments Results on the Effect of Language Normalization Scheme on Detection Approaches --- p.106 / Chapter 7.8 --- TDT2000 Topic Detection Result --- p.110 / Chapter 8 --- Conclusions and Future Works --- p.112 / Chapter 8.1 --- Conclusions --- p.112 / Chapter 8.2 --- Future Work --- p.114 / Bibliography --- p.115 / Chapter A --- List of Topics annotated for TDT2 Corpus --- p.121 / Chapter B --- Significant Test Results --- p.124

Huang Ruizhang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 110-114). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Defitition of Topic and Event --- p.2 / Chapter 1.2 --- Event and Topic Discovery --- p.2 / Chapter 1.2.1 --- Problem Definition --- p.2 / Chapter 1.2.2 --- Characteristics of the Discovery Problems --- p.3 / Chapter 1.2.3 --- Our Contributions --- p.5 / Chapter 1.3 --- Story Link Detection --- p.5 / Chapter 1.3.1 --- Problem Definition --- p.5 / Chapter 1.3.2 --- Our Contributions --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- University of Massachusetts (UMass) --- p.8 / Chapter 2.1.1 --- Topic Detection Approach --- p.8 / Chapter 2.1.2 --- Story Link Detection Approach --- p.9 / Chapter 2.2 --- BBN Technologies --- p.10 / Chapter 2.3 --- IBM Research Center --- p.11 / Chapter 2.4 --- Carnegie Mellon University (CMU) --- p.12 / Chapter 2.4.1 --- Topic Detection Approach --- p.12 / Chapter 2.4.2 --- Story Link Detection Approach --- p.14 / Chapter 2.5 --- National Taiwan University (NTU) --- p.14 / Chapter 2.5.1 --- Topic Detection Approach --- p.14 / Chapter 2.5.2 --- Story Link Detection Approach --- p.15 / Chapter 3 --- System Overview --- p.17 / Chapter 3.1 --- News Sources --- p.18 / Chapter 3.2 --- Story Preprocessing --- p.24 / Chapter 3.3 --- Information Extraction --- p.25 / Chapter 3.4 --- Gloss Translation --- p.26 / Chapter 3.5 --- Term Weight Calculation --- p.30 / Chapter 3.6 --- Event And Topic Discovery --- p.31 / Chapter 3.7 --- Story Link Detection --- p.33 / Chapter 4 --- Event And Topic Discovery --- p.34 / Chapter 4.1 --- Overview of Event and Topic discovery --- p.34 / Chapter 4.2 --- Event Discovery Component --- p.37 / Chapter 4.2.1 --- Overview of Event Discovery Algorithm --- p.37 / Chapter 4.2.2 --- Similarity Calculation --- p.39 / Chapter 4.2.3 --- Story and Event Combination --- p.43 / Chapter 4.2.4 --- Event Discovery Output --- p.44 / Chapter 4.3 --- Topic Discovery Component --- p.45 / Chapter 4.3.1 --- Overview of Topic Discovery Algorithm --- p.47 / Chapter 4.3.2 --- Relevance Model --- p.47 / Chapter 4.3.3 --- Event and Topic Combination --- p.50 / Chapter 4.3.4 --- Topic Discovery Output --- p.50 / Chapter 5 --- Event And Topic Discovery Experimental Results --- p.54 / Chapter 5.1 --- Testing Corpus --- p.54 / Chapter 5.2 --- Evaluation Methodology --- p.56 / Chapter 5.3 --- Experimental Results on Event Discovery --- p.58 / Chapter 5.3.1 --- Parameter Tuning --- p.58 / Chapter 5.3.2 --- Event Discovery Result --- p.59 / Chapter 5.4 --- Experimental Results on Topic Discovery --- p.62 / Chapter 5.4.1 --- Parameter Tuning --- p.64 / Chapter 5.4.2 --- Topic Discovery Results --- p.64 / Chapter 6 --- Story Link Detection --- p.67 / Chapter 6.1 --- Topic Types --- p.67 / Chapter 6.2 --- Overview of Link Detection Component --- p.68 / Chapter 6.3 --- Automatic Topic Type Categorization --- p.70 / Chapter 6.3.1 --- Training Data Preparation --- p.70 / Chapter 6.3.2 --- Feature Selection --- p.72 / Chapter 6.3.3 --- Training and Tuning Categorization Model --- p.73 / Chapter 6.4 --- Link Detection Algorithm --- p.74 / Chapter 6.4.1 --- Story Component Weight --- p.74 / Chapter 6.4.2 --- Story Link Similarity Calculation --- p.76 / Chapter 6.5 --- Story Link Detection Output --- p.77 / Chapter 7 --- Link Detection Experimental Results --- p.80 / Chapter 7.1 --- Testing Corpus --- p.80 / Chapter 7.2 --- Topic Type Categorization Result --- p.81 / Chapter 7.3 --- Link Detection Evaluation Methodology --- p.82 / Chapter 7.4 --- Experimental Results on Link Detection --- p.83 / Chapter 7.4.1 --- Language Normalization Factor Tuning --- p.83 / Chapter 7.4.2 --- Link Detection Performance --- p.90 / Chapter 7.4.3 --- Link Detection Performance Breakdown --- p.91 / Chapter 8 --- Conclusions and Future Work --- p.95 / Chapter 8.1 --- Conclusions --- p.95 / Chapter 8.2 --- Future Work --- p.96 / Chapter A --- List of Topic Title Annotated for TDT3 corpus by LDC --- p.98 / Chapter B --- List of Manually Annotated Events for TDT3 Corpus --- p.104 / Bibliography --- p.114