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

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

This study is concerned with determining the effects that video display terminal use had on reporters' and editors' attitudes toward their jobs and the machines themselves. Data for this investigation were obtained with questionnaires returned from seventy-one reporters and editors who use video terminals in their daily work. Questionnaire data were supplemented with interview data from thirteen questionnaire respondents, Ten hypotheses in five categories were tested with the t test. Four additional hypotheses were tested with raw data. Findings showed that video terminal use enhanced perceived job professionalism and made respondents think they should make more money. Attitudes toward video terminals improved after use of the devices, and respondents recognized the value of video terminal training in college,

The aim of this study was to determine the factors influencing readership of electronic news via the Internet. The status of printed news in the changing news environment was also investigated in the light of increasing electronic news readership. In order to achieve this aim, current electronic news readers were probed on their Internet news readership. The findings indicated that although traditional printed news was still very widely read, the shift amongst Internet users seemed to be towards reading more electronic news in the future. This study found that the two most significant factors that will lead to an increase in electronic news readership are the following: • This medium being accessed free of charge, i.e. where no monthly Internet subscriptions need to be paid. This has been achieved through free Internet access via ABSA since 16 February 2001; and • an improvement in the speed of access. The fact that traditional printed newspapers can be read in an informal environment seemed to be the single most important factor in maintaining its popularity. / Thesis (M.Com.)-University of Durban-Westville, 2001.

The Internet and World Wide Web have become dominant newsgathering tools in a sholi period of time. While the body of research, particularly in the First World, has developed quickly along with the Web, many unanswered questions remain on how journalists in developing countries make use of the Internet for newsgathering purposes. This study combined social constructivist theory with the socio-organisational and cultural approaches to news production in order to critically investigate how journalists at The Post newspaper in Zambia relate to, and make use of, the Internet as a newsgathering resource, in the context of Third World conditions. The study critiqued technological detelminism perspectives on journalists' use of the new information technology. The technological determinism theory, which has largely been advanced by some scholars from the developed world, takes a celebratory approach to journalists' use of the Internet in the newsroom. Using qualitative semi-structured interviews and observations, the study established that while journalists at The Post acknowledged the lnternet's potential in news gathering, factors such as unreliable telecommunications infrastructure, poor Internet skills, lack of local content on the World Wide Web, and organisational and occupational demands inhibited the use of the Internet as a journalistic newsgathering resource. The study established further that online reporting is only a tool within the broader news gathering and production process; and in the case of The Post, it does not replace the traditional news gathering techniques used by journalists, particularly direct contacts with human sources. The respondents cited face-to-face interviews, a traditional means of newsgathering, as the main driving force in news gathering routines at the newspaper. However, although the respondents saw some mixed blessings in the Internet as a reporting tool, they also believed that the benefits outweighed the problems. / KMBT_363 / Adobe Acrobat 9.54 Paper Capture Plug-in

This study set out to investigate Grocott’s Mail journalists’ appropriation of information and communication technologies in news-gathering and processing using the social shaping of technology as a theoretical lens. It mainly focuses on digital ICTs that journalists use in news-gathering and processing including the Internet, electronic mail, and mobile telephony. Grocott’s Mail is a small-scale newspaper based in Grahamstown, South Africa. Using qualitative research method and the case study as its sub-method, the research reveals that Grocott’s Mail journalists’ appropriation of ICTs involves various opportunities and challenges in news-gathering and processing. In terms of the state of the existing technological infrastructure, the study reveals that since it embraced the digital ICTs in 2003, Grocott’s Mail boasts an adequate ICT infrastructure for a small-scale African newspaper with 30 PCs and one laptop, and professional software for 28 permanent employees. On the other hand, the research reveals serious constraints with the existing ICTs: a huge need for staff training and capacity building to fully utilise the ICTs, and the need to look for ways of raising funds to either upgrade or replace the existing ICTs. Grocott’s Mail journalists use the Internet to do background research on news stories, to verify the accuracy of information, and to check competition across other media. These are the merits of the Internet in news-gathering and processing. On the other hand, there are specific unintended consequences of the Internet such as wasting the company’s working time, and its use leading to lazy/press release journalism. Informants unanimously indicate that the main problems of the Internet are heavy dependence on other online news-sources and wasting time on online entertainment. In terms of using email in news-gathering, the research finds email technology as having advantages such as being a tool of flexibility and speed, a tool for email interviews, and as a technology that promotes participatory journalism. On the other hand, challenges related to email include its limitations because of what interviewees view as its supplementary and small-scale use because of its low contextual richness as opposed to face to face interviews. In terms of cellular telephony, the study finds that regardless of the ubiquity of cell phones and cell phone networks, they have not yet been deployed in news-gathering and processing due to various constraints. These are cell phones not being a big factor in reporting, lack of a proper funding and refunding scheme, prevalence of negative attitudes towards cell phones, and lack of a business model. Thus, cellular phones seem to have little or no relevance in news-gathering and processing at Grocott’s Mail presently.