Siu, Kai-Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 99-106). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Thesis Outline --- p.5 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Natural Language Understanding --- p.6 / Chapter 2.1.1 --- Rule-based Approaches --- p.7 / Chapter 2.1.2 --- Stochastic Approaches --- p.8 / Chapter 2.1.3 --- Phrase-Spotting Approaches --- p.9 / Chapter 2.2 --- Grammar Induction --- p.10 / Chapter 2.2.1 --- Semantic Classification Trees --- p.11 / Chapter 2.2.2 --- Simulated Annealing --- p.12 / Chapter 2.2.3 --- Bayesian Grammar Induction --- p.12 / Chapter 2.2.4 --- Statistical Grammar Induction --- p.13 / Chapter 2.3 --- Machine Translation --- p.14 / Chapter 2.3.1 --- Rule-based Approach --- p.15 / Chapter 2.3.2 --- Statistical Approach --- p.15 / Chapter 2.3.3 --- Example-based Approach --- p.16 / Chapter 2.3.4 --- Knowledge-based Approach --- p.16 / Chapter 2.3.5 --- Evaluation Method --- p.19 / Chapter 3 --- Semi-Automatic Grammar Induction --- p.20 / Chapter 3.1 --- Agglomerative Clustering --- p.20 / Chapter 3.1.1 --- Spatial Clustering --- p.21 / Chapter 3.1.2 --- Temporal Clustering --- p.24 / Chapter 3.1.3 --- Free Parameters --- p.26 / Chapter 3.2 --- Post-processing --- p.27 / Chapter 3.3 --- Chapter Summary --- p.29 / Chapter 4 --- Application to the ATIS Domain --- p.30 / Chapter 4.1 --- The ATIS Domain --- p.30 / Chapter 4.2 --- Parameters Selection --- p.32 / Chapter 4.3 --- Unsupervised Grammar Induction --- p.35 / Chapter 4.4 --- Prior Knowledge Injection --- p.40 / Chapter 4.5 --- Evaluation --- p.43 / Chapter 4.5.1 --- Parse Coverage in Understanding --- p.45 / Chapter 4.5.2 --- Parse Errors --- p.46 / Chapter 4.5.3 --- Analysis --- p.47 / Chapter 4.6 --- Chapter Summary --- p.49 / Chapter 5 --- Portability to Chinese --- p.50 / Chapter 5.1 --- Corpus Preparation --- p.50 / Chapter 5.1.1 --- Tokenization --- p.51 / Chapter 5.2 --- Experiments --- p.52 / Chapter 5.2.1 --- Unsupervised Grammar Induction --- p.52 / Chapter 5.2.2 --- Prior Knowledge Injection --- p.56 / Chapter 5.3 --- Evaluation --- p.58 / Chapter 5.3.1 --- Parse Coverage in Understanding --- p.59 / Chapter 5.3.2 --- Parse Errors --- p.60 / Chapter 5.4 --- Grammar Comparison Across Languages --- p.60 / Chapter 5.5 --- Chapter Summary --- p.64 / Chapter 6 --- Bi-directional Machine Translation --- p.65 / Chapter 6.1 --- Bilingual Dictionary --- p.67 / Chapter 6.2 --- Concept Alignments --- p.68 / Chapter 6.3 --- Translation Procedures --- p.73 / Chapter 6.3.1 --- The Matching Process --- p.74 / Chapter 6.3.2 --- The Searching Process --- p.76 / Chapter 6.3.3 --- Heuristics to Aid Translation --- p.81 / Chapter 6.4 --- Evaluation --- p.82 / Chapter 6.4.1 --- Coverage --- p.83 / Chapter 6.4.2 --- Performance --- p.86 / Chapter 6.5 --- Chapter Summary --- p.89 / Chapter 7 --- Conclusions --- p.90 / Chapter 7.1 --- Summary --- p.90 / Chapter 7.2 --- Future Work --- p.92 / Chapter 7.2.1 --- Suggested Improvements on Grammar Induction Process --- p.92 / Chapter 7.2.2 --- Suggested Improvements on Bi-directional Machine Trans- lation --- p.96 / Chapter 7.2.3 --- Domain Portability --- p.97 / Chapter 7.3 --- Contributions --- p.97 / Bibliography --- p.99 / Chapter A --- Original SQL Queries --- p.107 / Chapter B --- Induced Grammar --- p.109 / Chapter C --- Seeded Categories --- p.111

Lin Wai-yip. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 114-123). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Information Extraction --- p.1 / Chapter 1.2 --- IE from Semi-structured Documents --- p.3 / Chapter 1.3 --- Thesis Contributions --- p.7 / Chapter 1.4 --- Thesis Organization --- p.9 / Chapter 2 --- Related Work --- p.11 / Chapter 2.1 --- Existing Approaches --- p.11 / Chapter 2.2 --- Limitations of Existing Approaches --- p.18 / Chapter 2.3 --- Our HISER Approach --- p.20 / Chapter 3 --- System Overview --- p.23 / Chapter 3.1 --- Hierarchical record Structure and Extraction Rule learning (HISER) --- p.23 / Chapter 3.2 --- Hierarchical Record Structure --- p.29 / Chapter 3.3 --- Extraction Rule --- p.29 / Chapter 3.4 --- Wrapper Adaptation --- p.32 / Chapter 4 --- Automatic Hierarchical Record Structure Construction --- p.34 / Chapter 4.1 --- Motivation --- p.34 / Chapter 4.2 --- Hierarchical Record Structure Representation --- p.36 / Chapter 4.3 --- Constructing Hierarchical Record Structure --- p.38 / Chapter 5 --- Extraction Rule Induction --- p.43 / Chapter 5.1 --- Rule Representation --- p.43 / Chapter 5.2 --- Extraction Rule Induction Algorithm --- p.47 / Chapter 6 --- Experimental Results of Wrapper Learning --- p.54 / Chapter 6.1 --- Experimental Methodology --- p.54 / Chapter 6.2 --- Results on Electronic Appliance Catalogs --- p.56 / Chapter 6.3 --- Results on Book Catalogs --- p.60 / Chapter 6.4 --- Results on Seminar Announcements --- p.62 / Chapter 7 --- Adapting Wrappers to Unseen Information Sources --- p.69 / Chapter 7.1 --- Motivation --- p.69 / Chapter 7.2 --- Support Vector Machines --- p.72 / Chapter 7.3 --- Feature Selection --- p.76 / Chapter 7.4 --- Automatic Annotation of Training Examples --- p.80 / Chapter 7.4.1 --- Building SVM Models --- p.81 / Chapter 7.4.2 --- Seeking Potential Training Example Candidates --- p.82 / Chapter 7.4.3 --- Classifying Potential Training Examples --- p.84 / Chapter 8 --- Experimental Results of Wrapper Adaptation --- p.86 / Chapter 8.1 --- Experimental Methodology --- p.86 / Chapter 8.2 --- Results on Electronic Appliance Catalogs --- p.89 / Chapter 8.3 --- Results on Book Catalogs --- p.93 / Chapter 9 --- Conclusions and Future Work --- p.97 / Chapter 9.1 --- Conclusions --- p.97 / Chapter 9.2 --- Future Work --- p.100 / Chapter A --- Sample Experimental Pages --- p.101 / Chapter B --- Detailed Experimental Results of Wrapper Adaptation of HISER --- p.109 / Bibliography --- p.114

Chan, Shuk Fong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 114-122). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Thesis Contributions --- p.5 / Chapter 1.3 --- Thesis Outline --- p.9 / Chapter 2 --- Background --- p.10 / Chapter 2.1 --- Mixed-Initiative Interactions --- p.11 / Chapter 2.2 --- Mixed-Initiative Spoken Dialog Systems --- p.14 / Chapter 2.2.1 --- Finite-state Networks --- p.16 / Chapter 2.2.2 --- Form-based Approaches --- p.17 / Chapter 2.2.3 --- Sequential Decision Approaches --- p.18 / Chapter 2.2.4 --- Machine Learning Approaches --- p.20 / Chapter 2.3 --- Understanding Mixed-Initiative Dialogs --- p.24 / Chapter 2.4 --- Cooperative Response Generation --- p.26 / Chapter 2.4.1 --- Plan-based Approach --- p.27 / Chapter 2.4.2 --- Constraint-based Approach --- p.28 / Chapter 2.5 --- Chapter Summary --- p.29 / Chapter 3 --- Mixed-Initiative Dialog Management in the ISIS system --- p.30 / Chapter 3.1 --- The ISIS Domain --- p.31 / Chapter 3.1.1 --- System Overview --- p.31 / Chapter 3.1.2 --- Domain-Specific Constraints --- p.33 / Chapter 3.2 --- Discourse and Dialog --- p.34 / Chapter 3.2.1 --- Discourse Inheritance --- p.37 / Chapter 3.2.2 --- Mixed-Initiative Dialogs --- p.41 / Chapter 3.3 --- Challenges and New Directions --- p.45 / Chapter 3.3.1 --- A Learning System --- p.46 / Chapter 3.3.2 --- Combining Interaction and Delegation Subdialogs --- p.49 / Chapter 3.4 --- Chapter Summary --- p.57 / Chapter 4 --- Understanding Mixed-Initiative Human-Human Dialogs --- p.59 / Chapter 4.1 --- The CU Restaurants Domain --- p.60 / Chapter 4.2 --- "Task Goals, Dialog Acts, Categories and Annotation" --- p.61 / Chapter 4.2.1 --- Task Goals and Dialog Acts --- p.61 / Chapter 4.2.2 --- Semantic and Syntactic Categories --- p.64 / Chapter 4.2.3 --- Annotating the Training Sentences --- p.65 / Chapter 4.3 --- Selective Inheritance Strategy --- p.67 / Chapter 4.3.1 --- Category Inheritance Rules --- p.67 / Chapter 4.3.2 --- Category Refresh Rules --- p.73 / Chapter 4.4 --- Task Goal and Dialog Act Identification --- p.78 / Chapter 4.4.1 --- Belief Networks Development --- p.78 / Chapter 4.4.2 --- Varying the Input Dimensionality --- p.80 / Chapter 4.4.3 --- Evaluation --- p.80 / Chapter 4.5 --- Procedure for Discourse Inheritance --- p.83 / Chapter 4.6 --- Chapter Summary --- p.86 / Chapter 5 --- Cooperative Response Generation in Mixed-Initiative Dialog Modeling --- p.88 / Chapter 5.1 --- System Overview --- p.89 / Chapter 5.1.1 --- State Space Generation --- p.89 / Chapter 5.1.2 --- Task Goal and Dialog Act Generation for System Response --- p.92 / Chapter 5.1.3 --- Response Frame Generation --- p.93 / Chapter 5.1.4 --- Text Generation --- p.100 / Chapter 5.2 --- Experiments and Results --- p.100 / Chapter 5.2.1 --- Subjective Results --- p.103 / Chapter 5.2.2 --- Objective Results --- p.105 / Chapter 5.3 --- Chapter Summary --- p.105 / Chapter 6 --- Conclusions --- p.108 / Chapter 6.1 --- Summary --- p.108 / Chapter 6.2 --- Contributions --- p.110 / Chapter 6.3 --- Future Work --- p.111 / Bibliography --- p.113 / Chapter A --- Domain-Specific Task Goals in CU Restaurants Domain --- p.123 / Chapter B --- Full list of VERBMOBIL-2 Dialog Acts --- p.124 / Chapter C --- Dialog Acts for Customer Requests and Waiter Responses in CU Restaurants Domain --- p.125 / Chapter D --- The Two Grammers for Task Goal and Dialog Act Identifi- cation --- p.130 / Chapter E --- Category Inheritance Rules --- p.143 / Chapter F --- Category Refresh Rules --- p.149 / Chapter G --- Full list of Response Trigger Words --- p.154 / Chapter H --- Evaluation Test Questionnaire for Dialog System in CU Restaurants Domain --- p.159 / Chapter I --- Details of the statistical testing Regarding Grice's Maxims and User Satisfaction --- p.161

Wong, Chin Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 137-143). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objectives --- p.3 / Chapter 1.2 --- Thesis Outline --- p.5 / Chapter 2 --- Background in Natural Language Understanding --- p.6 / Chapter 2.1 --- Rule-based Approaches --- p.7 / Chapter 2.2 --- Corpus-based Approaches --- p.8 / Chapter 2.2.1 --- Stochastic Approaches --- p.8 / Chapter 2.2.2 --- Phrase-spotting Approaches --- p.9 / Chapter 2.3 --- The ATIS Domain --- p.10 / Chapter 2.3.1 --- Chinese Corpus Preparation --- p.11 / Chapter 3 --- Semi-automatic Grammar Induction - Baseline Approach --- p.13 / Chapter 3.1 --- Background in Grammar Induction --- p.13 / Chapter 3.1.1 --- Simulated Annealing --- p.14 / Chapter 3.1.2 --- Bayesian Grammar Induction --- p.14 / Chapter 3.1.3 --- Probabilistic Grammar Acquisition --- p.15 / Chapter 3.2 --- Semi-automatic Grammar Induction 一 Baseline Approach --- p.16 / Chapter 3.2.1 --- Spatial Clustering --- p.16 / Chapter 3.2.2 --- Temporal Clustering --- p.18 / Chapter 3.2.3 --- Post-processing --- p.19 / Chapter 3.2.4 --- Four Aspects for Enhancements --- p.20 / Chapter 3.3 --- Chapter Summary --- p.22 / Chapter 4 --- Semi-automatic Grammar Induction - Enhanced Approach --- p.23 / Chapter 4.1 --- Evaluating Induced Grammars --- p.24 / Chapter 4.2 --- Stopping Criterion --- p.26 / Chapter 4.2.1 --- Cross-checking with Recall Values --- p.29 / Chapter 4.3 --- Improvements on Temporal Clustering --- p.32 / Chapter 4.3.1 --- Evaluation --- p.39 / Chapter 4.4 --- Improvements on Spatial Clustering --- p.46 / Chapter 4.4.1 --- Distance Measures --- p.48 / Chapter 4.4.2 --- Evaluation --- p.57 / Chapter 4.5 --- Enhancements based on Intelligent Selection --- p.62 / Chapter 4.5.1 --- Informed Selection between Spatial Clustering and Tem- poral Clustering --- p.62 / Chapter 4.5.2 --- Selecting the Number of Clusters Per Iteration --- p.64 / Chapter 4.5.3 --- An Example for Intelligent Selection --- p.64 / Chapter 4.5.4 --- Evaluation --- p.68 / Chapter 4.6 --- Chapter Summary --- p.71 / Chapter 5 --- Bidirectional Machine Translation using Induced Grammars ´ؤBaseline Approach --- p.73 / Chapter 5.1 --- Background in Machine Translation --- p.75 / Chapter 5.1.1 --- Rule-based Machine Translation --- p.75 / Chapter 5.1.2 --- Statistical Machine Translation --- p.76 / Chapter 5.1.3 --- Knowledge-based Machine Translation --- p.77 / Chapter 5.1.4 --- Example-based Machine Translation --- p.78 / Chapter 5.1.5 --- Evaluation --- p.79 / Chapter 5.2 --- Baseline Configuration on Bidirectional Machine Translation System --- p.84 / Chapter 5.2.1 --- Bilingual Dictionary --- p.84 / Chapter 5.2.2 --- Concept Alignments --- p.85 / Chapter 5.2.3 --- Translation Process --- p.89 / Chapter 5.2.4 --- Two Aspects for Enhancements --- p.90 / Chapter 5.3 --- Chapter Summary --- p.91 / Chapter 6 --- Bidirectional Machine Translation ´ؤ Enhanced Approach --- p.92 / Chapter 6.1 --- Concept Alignments --- p.93 / Chapter 6.1.1 --- Enhanced Alignment Scheme --- p.95 / Chapter 6.1.2 --- Experiment --- p.97 / Chapter 6.2 --- Grammar Checker --- p.100 / Chapter 6.2.1 --- Components for Grammar Checking --- p.101 / Chapter 6.3 --- Evaluation --- p.117 / Chapter 6.3.1 --- Bleu Score Performance --- p.118 / Chapter 6.3.2 --- Modified Bleu Score --- p.122 / Chapter 6.4 --- Chapter Summary --- p.130 / Chapter 7 --- Conclusions --- p.131 / Chapter 7.1 --- Summary --- p.131 / Chapter 7.2 --- Contributions --- p.134 / Chapter 7.3 --- Future work --- p.136 / Bibliography --- p.137 / Chapter A --- Original SQL Queries --- p.144 / Chapter B --- Seeded Categories --- p.146 / Chapter C --- 3 Alignment Categories --- p.147 / Chapter D --- Labels of Syntactic Structures in Grammar Checker --- p.148

Chan Ki, Cecia. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 95-99). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Our Contributions --- p.4 / Chapter 1.2 --- Thesis Organization --- p.5 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Using Knowledge-based Inferences --- p.7 / Chapter 2.2 --- Using Linguistic Techniques --- p.8 / Chapter 2.2.1 --- Using Linguistic Clues --- p.8 / Chapter 2.2.2 --- Using Graphical Patterns --- p.9 / Chapter 2.2.3 --- Using Lexicon-syntactic Patterns of Causative Verbs --- p.10 / Chapter 2.2.4 --- Comparisons with Our Approach --- p.10 / Chapter 2.3 --- Discovery of Extraction Patterns for Extracting Relations --- p.11 / Chapter 2.3.1 --- Snowball system --- p.12 / Chapter 2.3.2 --- DIRT system --- p.12 / Chapter 2.3.3 --- Comparisons with Our Approach --- p.13 / Chapter 3 --- Semantic Expectation-based Knowledge Extraction --- p.14 / Chapter 3.1 --- Semantic Expectations --- p.14 / Chapter 3.2 --- Semantic Template --- p.16 / Chapter 3.2.1 --- Causation Semantic Template --- p.16 / Chapter 3.3 --- Sentence Templates --- p.17 / Chapter 3.4 --- Consequence and Reason Templates --- p.22 / Chapter 3.5 --- Causation Knowledge Extraction Framework --- p.25 / Chapter 3.5.1 --- Template Design --- p.25 / Chapter 3.5.2 --- Sentence Screening --- p.27 / Chapter 3.5.3 --- Semantic Processing --- p.28 / Chapter 4 --- Using Thesaurus and Pattern Discovery for SEKE --- p.33 / Chapter 4.1 --- Using a Thesaurus --- p.34 / Chapter 4.2 --- Pattern Discovery --- p.37 / Chapter 4.2.1 --- Use of Semantic Expectation-based Knowledge Extraction --- p.37 / Chapter 4.2.2 --- Use of Part of Speech Information --- p.39 / Chapter 4.2.3 --- Pattern Representation --- p.39 / Chapter 4.2.4 --- Constructing the Patterns --- p.40 / Chapter 4.2.5 --- Merging the Patterns --- p.43 / Chapter 4.3 --- Pattern Matching --- p.44 / Chapter 4.3.1 --- Matching Score --- p.46 / Chapter 4.3.2 --- Support of Patterns --- p.48 / Chapter 4.3.3 --- Relevancy of Sentence Templates --- p.48 / Chapter 4.4 --- Applying the Newly Discovered Patterns --- p.49 / Chapter 5 --- Applying SEKE on Hong Kong Stock Market Domain --- p.52 / Chapter 5.1 --- Template Design --- p.53 / Chapter 5.1.1 --- Semantic Templates --- p.53 / Chapter 5.1.2 --- Sentence Templates --- p.53 / Chapter 5.1.3 --- Consequence and Reason Templates: --- p.55 / Chapter 5.2 --- Pattern Discovery --- p.58 / Chapter 5.2.1 --- Support of Patterns --- p.58 / Chapter 5.2.2 --- Relevancy of Sentence Templates --- p.58 / Chapter 5.3 --- Causation Knowledge Extraction Result --- p.58 / Chapter 5.3.1 --- Evaluation Approach --- p.61 / Chapter 5.3.2 --- Parameter Investigations --- p.61 / Chapter 5.3.3 --- Experimental Results --- p.65 / Chapter 5.3.4 --- Knowledge Discovered --- p.68 / Chapter 5.3.5 --- Parameter Effect --- p.75 / Chapter 6 --- Applying SEKE on Global Warming Domain --- p.80 / Chapter 6.1 --- Template Design --- p.80 / Chapter 6.1.1 --- Semantic Templates --- p.81 / Chapter 6.1.2 --- Sentence Templates --- p.81 / Chapter 6.1.3 --- Consequence and Reason Templates --- p.83 / Chapter 6.2 --- Pattern Discovery --- p.85 / Chapter 6.2.1 --- Support of Patterns --- p.85 / Chapter 6.2.2 --- Relevancy of Sentence Templates --- p.85 / Chapter 6.3 --- Global Warming Domain Result --- p.85 / Chapter 6.3.1 --- Evaluation Approach --- p.85 / Chapter 6.3.2 --- Experimental Results --- p.88 / Chapter 6.3.3 --- Knowledge Discovered --- p.89 / Chapter 7 --- Conclusions and Future Directions --- p.92 / Chapter 7.1 --- Conclusions --- p.92 / Chapter 7.2 --- Future Directions --- p.93 / Bibliography --- p.95 / Chapter A --- Penn Treebank Part of Speech Tags --- p.100

Ho Chao Yang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 157-163). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgment --- p.iii / List of Figures --- p.viii / List of Tables --- p.xiv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Automatic Document Categorization --- p.1 / Chapter 1.2 --- Information Filtering --- p.3 / Chapter 1.3 --- Contributions --- p.6 / Chapter 1.4 --- Organization of the Thesis --- p.7 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Existing Automatic Document Categorization Approaches --- p.9 / Chapter 2.1.1 --- Rule-Based Approach --- p.10 / Chapter 2.1.2 --- Similarity-Based Approach --- p.13 / Chapter 2.2 --- Existing Information Filtering Approaches --- p.19 / Chapter 2.2.1 --- Information Filtering Systems --- p.19 / Chapter 2.2.2 --- Filtering in TREC --- p.21 / Chapter 3 --- Document Pre-Processing --- p.23 / Chapter 3.1 --- Document Representation --- p.23 / Chapter 3.2 --- Classification Scheme Learning Strategy --- p.26 / Chapter 4 --- A New Approach - IBRI --- p.31 / Chapter 4.1 --- Overview of Our New IBRI Approach --- p.31 / Chapter 4.2 --- The IBRI Representation and Definitions --- p.34 / Chapter 4.3 --- The IBRI Learning Algorithm --- p.37 / Chapter 5 --- IBRI Experiments --- p.43 / Chapter 5.1 --- Experimental Setup --- p.43 / Chapter 5.2 --- Evaluation Metric --- p.45 / Chapter 5.3 --- Results --- p.46 / Chapter 6 --- A New Approach - GIS --- p.50 / Chapter 6.1 --- Motivation of GIS --- p.50 / Chapter 6.2 --- Similarity-Based Learning --- p.51 / Chapter 6.3 --- The Generalized Instance Set Algorithm (GIS) --- p.58 / Chapter 6.4 --- Using GIS Classifiers for Classification --- p.63 / Chapter 6.5 --- Time Complexity --- p.64 / Chapter 7 --- GIS Experiments --- p.68 / Chapter 7.1 --- Experimental Setup --- p.68 / Chapter 7.2 --- Results --- p.73 / Chapter 8 --- A New Information Filtering Approach Based on GIS --- p.87 / Chapter 8.1 --- Information Filtering Systems --- p.87 / Chapter 8.2 --- GIS-Based Information Filtering --- p.90 / Chapter 9 --- Experiments on GIS-based Information Filtering --- p.95 / Chapter 9.1 --- Experimental Setup --- p.95 / Chapter 9.2 --- Results --- p.100 / Chapter 10 --- Conclusions and Future Work --- p.108 / Chapter 10.1 --- Conclusions --- p.108 / Chapter 10.2 --- Future Work --- p.110 / Chapter A --- Sample Documents in the corpora --- p.111 / Chapter B --- Details of Experimental Results of GIS --- p.120 / Chapter C --- Computational Time of Reuters-21578 Experiments --- p.141

Wai Ip Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 84-87). / Text in English; abstract also in Chinese. / Wai Ip Lam. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Information Retrieval --- p.5 / Chapter 2.1.1 --- Index Extraction --- p.6 / Chapter 2.1.2 --- Other Approaches to Extracting Indexes --- p.7 / Chapter 2.1.3 --- Conceptual Information Retrieval --- p.8 / Chapter 2.1.4 --- Information Extraction --- p.9 / Chapter 2.2 --- Natural Language Parsing --- p.9 / Chapter 2.2.1 --- Linguistics-based --- p.10 / Chapter 2.2.2 --- Corpus-based --- p.11 / Chapter 3 --- Concept Extraction --- p.13 / Chapter 3.1 --- Concepts in Sentences --- p.13 / Chapter 3.1.1 --- Semantic Structures and Themantic Roles --- p.13 / Chapter 3.1.2 --- Syntactic Functions --- p.14 / Chapter 3.2 --- Representing Concepts --- p.15 / Chapter 3.3 --- Application to Conceptual Information Retrieval --- p.18 / Chapter 3.4 --- Overview of Our Concept Extraction Model --- p.20 / Chapter 3.4.1 --- Corpus Training --- p.21 / Chapter 3.4.2 --- Sentence Analyzing --- p.22 / Chapter 4 --- Noun Phrase Detection --- p.23 / Chapter 4.1 --- Significance of Noun Phrase Detection --- p.23 / Chapter 4.1.1 --- Noun Phrases versus Terminals in Parse Trees --- p.23 / Chapter 4.1.2 --- Quantitative Analysis of Applying Noun Phrase Detection --- p.26 / Chapter 4.2 --- An Algorithm for Chinese Noun Phrase Partial Parsing --- p.28 / Chapter 4.2.1 --- The Hybrid Approach --- p.28 / Chapter 4.2.2 --- CNP3´ؤThe Chinese NP Partial Parser --- p.30 / Chapter 5 --- Rule Extraction and SVO Parsing --- p.35 / Chapter 5.1 --- Annotation of Corpora --- p.36 / Chapter 5.1.1 --- Components of Chinese Sentence Patterns --- p.36 / Chapter 5.1.2 --- Annotating Sentence Structures --- p.37 / Chapter 5.1.3 --- Illustrative Examples --- p.38 / Chapter 5.2 --- Parsing with Rules Obtained Directly from Corpora --- p.43 / Chapter 5.2.1 --- Extracting Rules --- p.43 / Chapter 5.2.2 --- Parsing --- p.44 / Chapter 5.3 --- Using Word Specific Information --- p.45 / Chapter 6 --- Generalization of Rules --- p.48 / Chapter 6.1 --- Essence of Chinese Linguistics on Generalization --- p.49 / Chapter 6.1.1 --- Classification of Chinese Sentence Patterns --- p.50 / Chapter 6.1.2 --- Revision of Chinese Verb Phrase Classification --- p.52 / Chapter 6.2 --- Initial Generalization --- p.53 / Chapter 6.2.1 --- Generalizing Rules --- p.55 / Chapter 6.2.2 --- Dealing with Alternative Results --- p.58 / Chapter 6.2.3 --- Parsing --- p.58 / Chapter 6.2.4 --- An illustrative Example --- p.59 / Chapter 6.3 --- Further Generalization --- p.60 / Chapter 7 --- Experiments on SVO Parsing --- p.62 / Chapter 7.1 --- Experimental Setup --- p.63 / Chapter 7.2 --- Effect of Adopting Noun Phrase Detection --- p.65 / Chapter 7.3 --- Results of Generalization --- p.68 / Chapter 7.4 --- Reliability Evaluation --- p.69 / Chapter 7.4.1 --- Covergence Sequence Tests --- p.69 / Chapter 7.4.2 --- Cross Evaluation Tests --- p.72 / Chapter 7.5 --- Overall Performance --- p.75 / Chapter 8 --- Conclusions --- p.79 / Chapter 8.1 --- Summary --- p.79 / Chapter 8.2 --- Contribution --- p.81 / Chapter 8.3 --- Future Directions --- p.81 / Chapter 8.3.1 --- Improvements in Parsing --- p.81 / Chapter 8.3.2 --- Concept Representations --- p.82 / Chapter 8.3.3 --- Non-IR Applications --- p.83 / Bibliography --- p.84 / Appendix --- p.88 / Chapter A --- The Extended Part of Speech Tag Set --- p.88

This thesis presents novel tasks and deep learning methods for machine reading comprehension and question answering with the goal of achieving natural language understanding. First, we consider a semantic parsing task where the model understands sentences and translates them into a logical form or instructions. We present a novel semi-supervised sequential autoencoder that considers language as a discrete sequential latent variable and semantic parses as the observations. This model allows us to leverage synthetically generated unpaired logical forms, and thereby alleviate the lack of supervised training data. We show the semi-supervised model outperforms a supervised model when trained with the additional generated data. Second, reading comprehension requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess reading comprehension ability, in both artificial agents and children learning to read. We propose a new, challenging, supervised reading comprehension task. We gather a large-scale dataset of news stories from the CNN and Daily Mail websites with Cloze-style questions created from the highlights. This dataset allows for the first time training deep learning models for reading comprehension. We also introduce novel attention-based models for this task and present qualitative analysis of the attention mechanism. Finally, following the recent advances in reading comprehension in both models and task design, we further propose a new task for understanding complex narratives, NarrativeQA, consisting of full texts of books and movie scripts. We collect human written questions and answers based on high-level plot summaries. This task is designed to encourage development of models for language understanding; it is designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard reading comprehension models struggle on the tasks presented here.

This thesis focuses on two important aspects of non-native reading comprehension: text readability assessment, which estimates the reading difficulty of a given text for L2 learners, and learner summarisation assessment, which evaluates the quality of learner summaries to assess their reading comprehension. We approach both tasks as supervised machine learning problems and present automated assessment systems that achieve state-of-the-art performance. We first address the task of text readability assessment for L2 learners. One of the major challenges for a data-driven approach to text readability assessment is the lack of significantly-sized level-annotated data aimed at L2 learners. We present a dataset of CEFR-graded texts tailored for L2 learners and look into a range of linguistic features affecting text readability. We compare the text readability measures for native and L2 learners and explore methods that make use of the more plentiful data aimed at native readers to help improve L2 readability assessment. We then present a summarisation task for evaluating non-native reading comprehension and demonstrate an automated summarisation assessment system aimed at evaluating the quality of learner summaries. We propose three novel machine learning approaches to assessing learner summaries. In the first approach, we examine using several NLP techniques to extract features to measure the content similarity between the reading passage and the summary. In the second approach, we calculate a similarity matrix and apply a convolutional neural network (CNN) model to assess the summary quality using the similarity matrix. In the third approach, we build an end-to-end summarisation assessment model using recurrent neural networks (RNNs). Further, we combine the three approaches to a single system using a parallel ensemble modelling technique. We show that our models outperform traditional approaches that rely on exact word match on the task and that our best model produces quality assessments close to professional examiners.

Medical Information Visualization makes optimized use of digitized data of medical records, e.g. Electronic Medical Record. This thesis is an extended work of Pictorial Information Visualization System (PIVS) developed by Yongji Jin (Jin, 2016) Jiaren Suo (Suo, 2017) which is a graphical visualization system by picturizing patient’s medical history summary depicting patients’ medical information in order to help patients and doctors to easily capture patients’ past and present conditions. The summary information has been manually entered into the interface where the information can be taken from clinical notes. This study proposes a methodology of automatically extracting medical information from patients’ clinical notes by using the techniques of Natural Language Processing in order to produce medical history summarization from past medical records. We develop a Named Entities Recognition system to extract the information of the medical imaging procedure (performance date, human body location, imaging results and so on) and medications (medication names, frequency and quantities) by applying the model of conditional random fields with three main features and others: word-based, part-of-speech, Metamap semantic features. Adding Metamap semantic features is a novel idea which raised the accuracy compared to previous studies. Our evaluation shows that our model has higher accuracy than others on medication extraction as a case study. For enhancing the accuracy of entities extraction, we also propose a methodology of Topic Segmentation to clinical notes using boundary detection by determining the difference of classification probabilities of subsequence sequences, which is different from the traditional Topic Segmentation approaches such as TextTiling, TopicTiling and Beeferman Statistical Model. With Topic Segmentation combined for Named Entities Extraction, we observed higher accuracy for medication extraction compared to the case without the segmentation. Finally, we also present a prototype of integrating our information extraction system with PIVS by simply building the database of interface coordinates and the terms of human body parts.