Transformational tagging for topic tracking in natural language.

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

Next-gen traffic surveillance: AI-assisted mobile traffic violation detection system

Road traffic accidents pose a significant global public health concern, leading to injuries, fatalities, and vehicle damage. Approximately 1,3 million people lose their lives daily due to traffic accidents [World Health Organization, 2022]. Addressing this issue requires accurate traffic law violation detection systems to ensure adherence to regulations. The integration of Artificial Intelligence algorithms, leveraging machine learning and computer vision, has facilitated the development of precise traffic rule enforcement. This paper illustrates how computer vision and machine learning enable the creation of robust algorithms for detecting various traffic violations. Our model, capable of identifying six common traffic infractions, detects red light violations, illegal use of breakdown lanes, violations of vehicle following distance, breaches of marked crosswalk laws, illegal parking, and parking on marked crosswalks. Utilizing online traffic footage and a self-mounted on-dash camera, we apply the YOLOv5 algorithm's detection module to identify traffic agents such as cars, pedestrians, and traffic signs, and the strongSORT algorithm for continuous interframe tracking. Six discrete algorithms analyze agents' behavior and trajectory to detect violations. Subsequently, an Identification Module extracts vehicle ID information, such as the license plate, to generate violation notices sent to relevant authorities

Automatic topic detection from news stories.

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