O foco deste trabalho esta, no processo automático de condensação de uma estrutura complexa de informação e de sua estruturação, para fazê-la apropriada para a expressão textual. A tese principal é que, sem um modelo de discurso, não podemos assegurar a preservação de uma idéia central, pois o processamento do discurso envolve não só a informação, como também metas comunicativas e critérios para ressaltar unidades de informação. Como resultado os métodos para produzir uma estrutura coerente de discurso de um sumário agregam tanto metas comunicativas quanto informações sobre o inter-relacionamentos entre as unidades de informação permitindo a organização do discurso com base em restrições progressivas de planejamento. Esse argumento tem duas implicações: a preservação da idéia central deve ser garantida em nível profundo de processamento e sua proeminência deve ser subordinada aos aspectos comunicativos e retóricos. Portanto, esta investigação se baseia em perspectivas intencionais e retóricas. Propomos um modelo de sumarização dirigido por objetivos, cuja função principal é mapear intenções em relações de coerência, observando ainda a dependência semântica indicada pela estrutura complexa de informação. As estruturas de discurso resultantes devem enfatizar a proposição central a veicular no discurso. Em termos teóricos, o aspecto inovador do modelo está na associação de relações de discurso em três níveis distintos de representação: intencionalidade. coerência e semântica. Em termos práticos, a solução proposta sugere o projeto de um planejador de textos que pode tornar a proposição central de um discurso a informação mais proeminente em uma estrutura de discurso e, assim, assegurar a preservação da idéia central durante a condensação de uma estrutura complexa de informação. Os resultados experimentais da aplicação desse modelo demonstram que é possível selecionar a informação relevante, distinguindo as unidades de conteúdo da estrutura original que são supérfluas ou complementares para a proposição central, e organizá-la coerentemente com o intuito de alcançar um objetivo comunicativo. Propomos a incorporação do modelo a um sumarizador automático cuja arquitetura é sugerida neste trabalho. / The focus of this work is on the automatic process of condensing a. complex information structure and structuring it in such a way as to make it appropriate for textual expression. The main thesis is that without a sound discourse model we cannot guarantee gist preservation because discourse processing comprises not only information, but also communicative goals and criteria to emphasize units of information. As a result, the methods to produce a coherent discourse structure of a summary aggregate both communicative goals and the inter-relationships between information units, allowing for discourse organization by progressively constraining planning decisions. Our thrust has two implications, namely that gist preservation must be guaranteed at the deep level of processing and gist proeminence must be subordinated to communicative and rhetorical settings. The current investigation thus relies on intentional and rhetorical perspectives. A goal-driven summarization model is proposed, whose main function is to map intentions onto coherence relations whilst still observing the semantic dependency indicated by the complex input structure. The resulting discourse structures must highlight the central proposition to be conveyed. In theoretical terms, the innovative contribution of the model relies on the association of discourse relations at three different levels of representation - the intentionality, coherence and semantics. In practical terms, the proposed solution allows for the design of a text planner that can make the central proposition of a discourse the most proeminent information in a discourse structure, thus ensuring the preservation of gist during the condensation of a complex information structure. The results of applying this model show that it is possible to both select relevant information by differentiating content units of the input structure that are superfluous or complementary to the central proposition and organize it coherently by aiming at achieving a communicative goal. The model is proposed to incorporate into an automatic summariser whose architecture suggested in this thesis.

O CleverCare é um framework para controle, gestão e orientação de pacientes que necessitam de acompanhamento médico contínuo. O sistema possui ferramentas de mineração de textos responsáveis por compreender o conteúdo das mensagens e integrar com serviços de mensagem para envio e recebimento das mesmas, onde inicia diálogos com o paciente para gerenciar atividades rotineiras personalizadas e permite, inclusive, ao paciente fazer perguntas a respeito de uma enfermidade ou condição clínica. Desta forma, a comunicação com o paciente é a base para o sucesso do CleverCare, o qual atualmente possui suporte para o português, atuando por meio de suporte e empoderando o paciente ao cuidado de sua saúde. Compreender as implicações lógicas e adaptações necessárias para a compreensão de textos em diferentes idiomas pode fornecer informações para a aplicação dos mesmos procedimentos a outros idiomas, correlacionando informações e estabelecendo lógicas para traduções e tratamento de termos específicos da área, permitindo atender a uma maior demanda de pacientes que necessitam de tratamento contínuo. Para o desenvolvimento do projeto foram utilizadas abordagens e técnicas visando a escalabilidade e expansão de idiomas de maneira dinâmica. Para isso além das decisões de alterações específicas do sistema foram utilizadas ferramentas como o NLTK para o aperfeiçoamento e realização das adaptações necessárias ao projeto, uma vez que essa ferramenta possui suporte a diversos idiomas e está em constante melhoria. Os resultados, analisados por meio de técnicas de acurácia, precisão e revocação, demonstram que a melhoria observada com as adaptações do sistema para suporte aos idiomas de interesse foram positivas e significativas, com aumento de 13% nos indicadores de revocação e acurácia e manutenção da precisão em 100%. Sendo assim, o CleverCare apresentou um bom desempenho e foi capaz de classificar corretamente as mensagens, permitindo ao sistema reconhecer e classificar corretamente diferentes idiomas. Esta solução permite ao sistema não apenas fazer o processamento de diálogos em português, inglês e espanhol, mas também ingressar no mercado internacional com a possibilidade de expansão e escalabilidade para outros idiomas / CleverCare is a framework for the control, management, and guidance of patients who need ongoing medical follow-up. The system has text-mining tools responsible for understanding the content of the messages and integrating with message services to send and receive messages, where it initiates dialogues with the patient to manage personalized routine activities and allows the patient to ask questions about them in relation to an illness or clinical condition. In this way, communication with the patient is the basis for the success of CleverCare, which currently has support for Portuguese, acting through support and empowering the patient to take care of their health. Understanding the logical implications and adaptations required for the understanding of texts in different languages can provide information for the application of the same procedures to other languages, correlating information and establishing logics for translations and treatment of specific terms of the area, allowing to supply a greater demand of patients who require continuous treatment. For the development of the project, it was used approaches and techniques aimed at scaling and language expansion in a dynamic way. For this in addition to the system-specific changes decisions tools like NLTK were used, aiming at the improvement and accomplishment of the necessary adaptations to the project, since this tool has support to several languages and is constantly improving. The results, analyzed using accuracy, precision and recall techniques, demonstrate that the improvement observed with the system adaptations to support the languages of interest were positive and significant, with an increase of 13% in recall and accuracy indicators and maintenance of 100% of precision. Thus, CleverCare performed well and was able to classify messages correctly, allowing the system to correctly recognize and classify different languages. This solution allows the system not only to process dialogues in Portuguese, English and Spanish, but also to enter the international market with the possibility of expansion and scalability for other languages

Wong Kit-pui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 117-119). / ACKNOWLEDGMENT --- p.4 / ABSTRACT --- p.6 / KEYWORDS --- p.7 / Chapter Chapter 1 --- INTRODUCTION --- p.9 / Chapter Chapter 2 --- APPROACH --- p.14 / Chapter 2.1 --- Challenges --- p.15 / Chapter 2.2 --- Knowledge Representation --- p.16 / Chapter 2.3 --- Proposed Information Model --- p.17 / Chapter 2.4 --- Restricted Language Set --- p.20 / Chapter Chapter 3 --- THEORY --- p.26 / Chapter 3.1 --- Features --- p.26 / Chapter 3.1.1 --- Superficial Details --- p.30 / Chapter 3.1.2 --- Hidden Details --- p.31 / Chapter 3.2 --- Matching Process --- p.36 / Chapter 3.2.1 --- Inexact Match --- p.37 / Chapter 3.2.2 --- An Illustration --- p.38 / Chapter 3.2.2.1 --- Stage 1 - Query Parsing --- p.39 / Chapter 3.2.2.2 --- Stage 2 - Gross Filtering --- p.41 / Chapter 3.2.2.3 --- Stage 3 - Fine Scoring --- p.42 / Chapter 3.3 --- Extending Knowledge --- p.46 / Chapter 3.3.1 --- Attributes with Intermediate Closeness --- p.47 / Chapter 3.3.2 --- Comparing Different Entities --- p.48 / Chapter 3.4 --- Putting Concepts to Work --- p.50 / Chapter Chapter 4 --- IMPLEMENTATION --- p.52 / Chapter 4.1 --- Overall Structure --- p.53 / Chapter 4.2 --- Choosing NL Parser --- p.55 / Chapter 4.3 --- Ambiguity --- p.56 / Chapter 4.4 --- Storing Knowledge --- p.59 / Chapter 4.4.1 --- Type Hierarchy --- p.60 / Chapter 4.4.1.1 --- Node Name --- p.61 / Chapter 4.4.1.2 --- Node Identity --- p.61 / Chapter 4.4.1.3 --- Operations --- p.68 / Chapter 4.4.1.3.1 --- Direct Edit --- p.68 / Chapter 4.4.1.3.2 --- Interactive Edit --- p.68 / Chapter 4.4.2 --- Implicit Features --- p.71 / Chapter 4.4.3 --- Database of Captions --- p.72 / Chapter 4.4.4 --- Explicit Features --- p.73 / Chapter 4.4.5 --- Transformation Map --- p.74 / Chapter Chapter 5 --- ILLUSTRATION --- p.78 / Chapter 5.1 --- Gloss Tags --- p.78 / Chapter 5.2 --- Parsing --- p.81 / Chapter 5.2.1 --- Resolving Nouns and Verbs --- p.81 / Chapter 5.2.2 --- Resolving Adjectives and Adverbs --- p.84 / Chapter 5.2.3 --- Normalizing Features --- p.89 / Chapter 5.2.4 --- Resolving Prepositions --- p.90 / Chapter 5.3 --- Matching --- p.93 / Chapter 5.3.1 --- Gross Filtering --- p.94 / Chapter 5.3.2 --- Fine Scoring --- p.96 / Chapter Chapter 6 --- DISCUSSION --- p.101 / Chapter 6.1 --- Performance Measures --- p.101 / Chapter 6.1.1 --- General Parameters --- p.101 / Chapter 6.1.2 --- Experiments --- p.103 / Chapter 6.1.2.1 --- Inexact Matching Behaviour --- p.103 / Chapter 6.1.2.2 --- Exact Matching Behaviour --- p.106 / Chapter 6.2 --- Difficulties --- p.108 / Chapter 6.3 --- Possible Improvement --- p.110 / Chapter 6.4 --- Conclusion --- p.112 / REFERENCES --- p.117 / APPENDICES --- p.121 / Appendix A Notation --- p.121 / Appendix B Glossary --- p.123 / Appendix C Proposed Feature Slots and Value --- p.126 / Appendix D Sample Captions and Queries --- p.128 / Appendix E Manual Pages --- p.130 / Appendix F Directory Structure --- p.136 / Appendix G Imported Toolboxes --- p.137 / Appendix H Program Listing --- p.140

by Lam Sze-sing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 119-123). / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Statement of Thesis --- p.5 / Chapter 1.3 --- Organization of this Thesis --- p.6 / Chapter CHAPTER 2 --- RELATED WORK --- p.8 / Chapter 2.1 --- Overview --- p.8 / Chapter 2.2 --- Corpus-Based Knowledge Acquisition --- p.12 / Chapter 2.3 --- Linguistic-Based Knowledge Acquisition --- p.18 / Chapter 2.3.1 --- Knowledge Acquisition from Standard Dictionaries --- p.18 / Chapter 2.3.2 --- Knowledge Acquisition from Standard Thesauri --- p.23 / Chapter 2.4 --- Remarks --- p.24 / Chapter CHAPTER 3 --- A METHOD TO EXTRACT THE INTER-WORD SEMANTIC RELATIONSHIP FROM《同義詞詞林》 --- p.25 / Chapter 3.1 --- Background --- p.25 / Chapter 3.1.1 --- Structure of《《同義詞詞林》 --- p.26 / Chapter 3.1.2 --- Knowledge Representation of a Machine Tractable Thesaurus --- p.28 / Chapter 3.1.3 --- Extracting the Semantic Knowledge by Simple Co-occurrence --- p.28 / Chapter 3.2 --- Association Network --- p.31 / Chapter 3.3 --- Semantic Association Model --- p.33 / Chapter 3.3.1 --- Problems with the Simple Co-occurrence Method --- p.34 / Chapter 3.3.2 --- Methodology of Semantic Association Model --- p.39 / Chapter 3.4 --- Inter-word Semantic Function ..… --- p.51 / Chapter CHAPTER 4 --- NOUN-VERB-NOUN COMPOUND WORD DETECTION : AN EXPERIMENT --- p.55 / Chapter 4.1 --- Overview --- p.56 / Chapter 4.2 --- N-V-N Compound Word Detection Model --- p.61 / Chapter 4.3 --- Experimental Results of N-V-N Compound Word Detection --- p.63 / Chapter CHAPTER 5 --- WORD SENSE DISAMBIGUATION : AN APPLICATION … --- p.66 / Chapter 5.1 --- Overview --- p.67 / Chapter 5.2 --- Word-Sense Disambiguation Model --- p.72 / Chapter 5.2.1 --- Linguistic Resource --- p.72 / Chapter 5.2.2 --- The LSD-C Algorithm --- p.73 / Chapter 5.2.3 --- LSD-C in Action --- p.78 / Chapter 5.3 --- Experimental Results of Word Sense Disambiguation --- p.83 / Chapter CHAPTER 6 --- CONCLUSIONS & FURTHER RESEARCH --- p.93 / Chapter 6.1 --- Conclusions --- p.93 / Chapter 6.2 --- Further Research --- p.96 / Chapter 6.2.1 --- Enriching the Knowledge --- p.96 / Chapter 6.2.2 --- Enhancing the N-V-N Compound Word Detection Model --- p.98 / Chapter 6.2.3 --- Enhancing the LSD-C Algorithm --- p.99 / APPENDICES --- p.101 / Appendix A - Dependency Grammar --- p.101 / Appendix B - Sample Articles from a Local Chinese Newspaper --- p.104 / Appendix C - Ambiguous Words with the Senses Given by《現代漢語詞 典》 --- p.108 / Appendix D - List of Stop Words for the Testing Samples --- p.117 / REFERENCES --- p.119

O CleverCare é um framework para controle, gestão e orientação de pacientes que necessitam de acompanhamento médico contínuo. O sistema possui ferramentas de mineração de textos responsáveis por compreender o conteúdo das mensagens e integrar com serviços de mensagem para envio e recebimento das mesmas, onde inicia diálogos com o paciente para gerenciar atividades rotineiras personalizadas e permite, inclusive, ao paciente fazer perguntas a respeito de uma enfermidade ou condição clínica. Desta forma, a comunicação com o paciente é a base para o sucesso do CleverCare, o qual atualmente possui suporte para o português, atuando por meio de suporte e empoderando o paciente ao cuidado de sua saúde. Compreender as implicações lógicas e adaptações necessárias para a compreensão de textos em diferentes idiomas pode fornecer informações para a aplicação dos mesmos procedimentos a outros idiomas, correlacionando informações e estabelecendo lógicas para traduções e tratamento de termos específicos da área, permitindo atender a uma maior demanda de pacientes que necessitam de tratamento contínuo. Para o desenvolvimento do projeto foram utilizadas abordagens e técnicas visando a escalabilidade e expansão de idiomas de maneira dinâmica. Para isso além das decisões de alterações específicas do sistema foram utilizadas ferramentas como o NLTK para o aperfeiçoamento e realização das adaptações necessárias ao projeto, uma vez que essa ferramenta possui suporte a diversos idiomas e está em constante melhoria. Os resultados, analisados por meio de técnicas de acurácia, precisão e revocação, demonstram que a melhoria observada com as adaptações do sistema para suporte aos idiomas de interesse foram positivas e significativas, com aumento de 13% nos indicadores de revocação e acurácia e manutenção da precisão em 100%. Sendo assim, o CleverCare apresentou um bom desempenho e foi capaz de classificar corretamente as mensagens, permitindo ao sistema reconhecer e classificar corretamente diferentes idiomas. Esta solução permite ao sistema não apenas fazer o processamento de diálogos em português, inglês e espanhol, mas também ingressar no mercado internacional com a possibilidade de expansão e escalabilidade para outros idiomas / CleverCare is a framework for the control, management, and guidance of patients who need ongoing medical follow-up. The system has text-mining tools responsible for understanding the content of the messages and integrating with message services to send and receive messages, where it initiates dialogues with the patient to manage personalized routine activities and allows the patient to ask questions about them in relation to an illness or clinical condition. In this way, communication with the patient is the basis for the success of CleverCare, which currently has support for Portuguese, acting through support and empowering the patient to take care of their health. Understanding the logical implications and adaptations required for the understanding of texts in different languages can provide information for the application of the same procedures to other languages, correlating information and establishing logics for translations and treatment of specific terms of the area, allowing to supply a greater demand of patients who require continuous treatment. For the development of the project, it was used approaches and techniques aimed at scaling and language expansion in a dynamic way. For this in addition to the system-specific changes decisions tools like NLTK were used, aiming at the improvement and accomplishment of the necessary adaptations to the project, since this tool has support to several languages and is constantly improving. The results, analyzed using accuracy, precision and recall techniques, demonstrate that the improvement observed with the system adaptations to support the languages of interest were positive and significant, with an increase of 13% in recall and accuracy indicators and maintenance of 100% of precision. Thus, CleverCare performed well and was able to classify messages correctly, allowing the system to correctly recognize and classify different languages. This solution allows the system not only to process dialogues in Portuguese, English and Spanish, but also to enter the international market with the possibility of expansion and scalability for other languages

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

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