Hu Ke. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 56-58). / Abstracts in English and Chinese.

Lam Hiu Sing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references. / Text in English; abstracts in English and Chinese. / Lam Hiu Sing. / Acknowledgement --- p.5 / Chapter I. --- Table of Content --- p.6 / Chapter II. --- Lists of Tables --- p.9 / Chapter III. --- Lists of Figures --- p.11 / Chapter Chapter 1 --- Introduction --- p.13 / Chapter 1.1 --- Intelligence gathering by surveillance --- p.13 / Chapter 1.2 --- Speech recognition and keyword spotting --- p.16 / Chapter 1.3 --- Audio indexing and searching --- p.18 / Chapter 1.3.1 --- Nature of audio sources --- p.18 / Chapter 1.3.2 --- Different searching objectives --- p.19 / Chapter 1.4 --- Objective of thesis --- p.22 / Chapter 1.5 --- Thesis outline --- p.23 / Chapter 1.6 --- References --- p.24 / Chapter Chapter 2 --- HMM-based Keyword Spotting System --- p.28 / Chapter 2.1 --- Statistical speech model --- p.28 / Chapter 2.1.1 --- Speech signal representations --- p.29 / Chapter 2.1.2 --- Acoustic modeling --- p.29 / Chapter 2.1.3 --- HMM message generation model --- p.32 / Chapter 2.2 --- Basics of keyword spotting --- p.34 / Chapter 2.2.1 --- Keyword and non-keyword modeling --- p.34 / Chapter 2.2.2 --- Language model --- p.37 / Chapter 2.2.3 --- Performance measure --- p.38 / Chapter 2.3 --- Keyword spotting applications --- p.39 / Chapter 2.3.1 --- Information query system --- p.40 / Chapter 2.3.2 --- Topic identification system --- p.41 / Chapter 2.3.3 --- Audio indexing and searching system --- p.42 / Chapter 2.3.4 --- Lexicon learning system --- p.42 / Chapter 2.4 --- Summary --- p.43 / Chapter 2.5 --- References --- p.44 / Chapter Chapter 3 --- Cantonese Characteristics --- p.49 / Chapter 3.1 --- Cantonese Dialect --- p.49 / Chapter 3.2 --- Phonological properties of Cantonese --- p.51 / Chapter 3.2.1 --- Initials and finals of Cantonese --- p.51 / Chapter 3.2.2 --- Tones of Cantonese --- p.54 / Chapter 3.3 --- Summary --- p.55 / Chapter 3.4 --- References --- p.55 / Chapter Chapter 4 --- System Configuration for Audio Search of Surveillance Data --- p.57 / Chapter 4.1 --- Audio Search of Surveillance Data --- p.57 / Chapter 4.2 --- Requirements and Specifications of the Proposed Audio Search System --- p.59 / Chapter 4.3 --- Proposed Audio Search System Architecture --- p.62 / Chapter 4.4 --- Summary --- p.65 / Chapter 4.5 --- References --- p.66 / Chapter Chapter 5 --- Development of a Keyword Spotting based Audio Indexing and Searching System --- p.67 / Chapter 5.1 --- Acoustic Models for Keywords and Fillers --- p.67 / Chapter 5.2 --- Adaptation mechanism --- p.76 / Chapter 5.2.1 --- Adaptation techniques --- p.76 / Chapter 5.2.2 --- Adaptation strategy for MLLR --- p.85 / Chapter 5.3 --- Language model --- p.86 / Chapter 5.4 --- Summary --- p.88 / Chapter 5.5 --- References --- p.88 / Chapter Chapter 6 --- System Evaluations --- p.93 / Chapter 6.1 --- Data for training and evaluation of the system --- p.94 / Chapter 6.1.1 --- Training Data --- p.94 / Chapter 6.1.2 --- Evaluation data --- p.95 / Chapter 6.1.3 --- Performance measure --- p.97 / Chapter 6.2 --- Cluster settings for MLLR adaptation --- p.98 / Chapter 6.3 --- Effects of word insertion penalty --- p.102 / Chapter 6.4 --- Acoustic modeling performance comparisons --- p.103 / Chapter 6.4.1 --- System robustness test --- p.104 / Chapter 6.4.2 --- The performance limit --- p.105 / Chapter 6.5 --- Overall System Performance --- p.107 / Chapter 6.6 --- Summary --- p.108 / Chapter 6.7 --- References --- p.108 / Chapter Chapter 7 --- Conclusions and Future Works --- p.109 / Chapter 7.1 --- Conclusions --- p.109 / Chapter 7.2 --- Future works --- p.110 / Chapter 7.2.1 --- Discriminative adaptation --- p.110 / Chapter 7.2.2 --- Pronunciation dictionary --- p.111 / Chapter 7.2.3 --- Channel effect --- p.111

Li Yuk Chi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves [112]-119). / Abstracts in English and Chinese. / Abstract --- p.2 / List of Figures --- p.8 / List of Tables --- p.12 / Chapter 1 --- Introduction --- p.17 / Chapter 1.1 --- Information Retrieval --- p.18 / Chapter 1.1.1 --- Information Retrieval Models --- p.19 / Chapter 1.1.2 --- Information Retrieval in English --- p.20 / Chapter 1.1.3 --- Information Retrieval in Chinese --- p.22 / Chapter 1.2 --- Spoken Document Retrieval --- p.24 / Chapter 1.2.1 --- Spoken Document Retrieval in English --- p.25 / Chapter 1.2.2 --- Spoken Document Retrieval in Chinese --- p.25 / Chapter 1.3 --- Previous Work --- p.28 / Chapter 1.4 --- Motivation --- p.32 / Chapter 1.5 --- Goals --- p.33 / Chapter 1.6 --- Thesis Organization --- p.34 / Chapter 2 --- Investigation Framework --- p.35 / Chapter 2.1 --- Indexing the Spoken Document Collection --- p.36 / Chapter 2.2 --- Query Processing --- p.37 / Chapter 2.3 --- Subword Indexing --- p.37 / Chapter 2.4 --- Robustness in Chinese Spoken Document Retrieval --- p.40 / Chapter 2.5 --- Retrieval --- p.40 / Chapter 2.6 --- Evaluation --- p.43 / Chapter 2.6.1 --- Average Inverse Rank --- p.43 / Chapter 2.6.2 --- Mean Average Precision --- p.44 / Chapter 3 --- Subword-based Chinese Spoken Document Retrieval --- p.46 / Chapter 3.1 --- The Cantonese Corpus --- p.48 / Chapter 3.2 --- Known-Item Retrieval --- p.49 / Chapter 3.3 --- Subword Formulation for Cantonese Spoken Document Retrieval --- p.50 / Chapter 3.4 --- Audio Indexing by Cantonese Speech Recognition --- p.52 / Chapter 3.4.1 --- Seed Models from Adapted Data --- p.52 / Chapter 3.4.2 --- Retraining Acoustic Models --- p.53 / Chapter 3.5 --- The Retrieval Model --- p.55 / Chapter 3.6 --- Experiments --- p.56 / Chapter 3.6.1 --- Setup and Observations --- p.57 / Chapter 3.6.2 --- Results Analysis --- p.58 / Chapter 3.7 --- Chapter Summary --- p.63 / Chapter 4 --- Robust Indexing and Retrieval Methods --- p.64 / Chapter 4.1 --- Query Expansion using Phonetic Confusion --- p.65 / Chapter 4.1.1 --- Syllable-Syllable Confusions from Recognition --- p.66 / Chapter 4.1.2 --- Experimental Setup and Observation --- p.67 / Chapter 4.2 --- Document Expansion --- p.71 / Chapter 4.2.1 --- The Side Collection for Expansion --- p.72 / Chapter 4.2.2 --- Detailed Procedures in Document Expansion --- p.72 / Chapter 4.2.3 --- Improvements due to Document Expansion --- p.73 / Chapter 4.3 --- Using both Query and Document Expansion --- p.75 / Chapter 4.4 --- Chapter Summary --- p.76 / Chapter 5 --- Cross-Language Spoken Document Retrieval --- p.78 / Chapter 5.1 --- The Topic Detection and Tracking Collection --- p.80 / Chapter 5.1.1 --- The Spoken Document Collection --- p.81 / Chapter 5.1.2 --- The Translingual Query --- p.82 / Chapter 5.1.3 --- The Side Collection --- p.82 / Chapter 5.1.4 --- Subword-based Indexing --- p.83 / Chapter 5.2 --- The Translingual Retrieval Task --- p.83 / Chapter 5.3 --- Machine Translated Query --- p.85 / Chapter 5.3.1 --- The Unbalanced Query --- p.85 / Chapter 5.3.2 --- The Balanced Query --- p.87 / Chapter 5.3.3 --- Results on the Weight Balancing Scheme --- p.88 / Chapter 5.4 --- Document Expansion from a Side Collection --- p.89 / Chapter 5.5 --- Performance Evaluation and Analysis --- p.91 / Chapter 5.6 --- Chapter Summary --- p.93 / Chapter 6 --- Summary and Future Work --- p.95 / Chapter 6.1 --- Future Directions --- p.97 / Chapter A --- Input format for the IR engine --- p.101 / Chapter B --- Preliminary Results on the Two Normalization Schemes --- p.102 / Chapter C --- Significance Tests --- p.103 / Chapter C.1 --- Query Expansions for Cantonese Spoken Document Retrieval --- p.103 / Chapter C.2 --- Document Expansion for Cantonese Spoken Document Retrieval --- p.105 / Chapter C.3 --- Balanced Query for Cross-Language Spoken Document Retrieval --- p.107 / Chapter C.4 --- Document Expansion for Cross-Language Spoken Document Retrieval --- p.107 / Chapter D --- The Use of an Unrelated Source for Expanding Spoken Doc- uments in Cantonese --- p.110 / Bibliography --- p.110

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

The development of tools and services for the realisation of the Semantic Web has been a very active field of research in recent years, with a strong focus on linking existing data. In the field of music information management, Semantic Web technologies may facilitate searching and browsing, and help to reveal relationships with data from other domains. At the same time, many algorithms have been developed to extract low and high-level features, which enable the user to analyse music and audio in detail. The use of semantics in the process of music production however is still a relatively new field of research. With computer systems and music processing applications becoming increasingly powerful and complex in their underlying structure, semantics can help musicians and producers in decision processes, and provide more natural interactions with the systems. Audio effects represent an integral part in modern music production. They modify an input signal and may be applied in order to enhance the perceived quality of a sound or to make more artistic changes to it in the composition process. Employing music information retrieval (MIR) and Semantic Web technologies specifically for the control of audio effects has the potential to be a significant step in their evolution. Detailed descriptions of the use of audio effects in a music production project can additionally facilitate the description of work flows and the reproducibility of production procedures, adding an additional layer of depth to MIR. We substantiate the hypothesis that the collection of audio related metadata during the production process is beneficial, by comparing the results of various feature extraction techniques on audio material before and after the application of audio effects. We develop a formal Semantic Web ontology for the domain of Audio Effects in the context of music production. The ontology enables the creation of detailed metadata about audio effects implementations within the Studio Ontology framework for use in music production projects. The ontology contains inter-linkable classification systems based on different criteria constituting an interdisciplinary classification. Finally, we evaluate the ontology and present several use cases and applications, such as adaptive audio effects using and creating semantic metadata.

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

Typescript (photocopy). / Digitized by Kansas Correctional Industries

The knowledge available to be exploited by text classification and information retrieval systems has significantly changed, both in nature and quantity, in the last years. Nowadays, there are several sources of information that can potentially improve the classification process, and systems should be able to adapt to incorporate multiple sources of available data in different formats. This fact is specially important in environments where the required information changes rapidly, and its utility may be contingent on timely implementation. For these reasons, the importance of adaptability and flexibility in information systems is rapidly growing. Current systems are usually developed for specific scenarios. As a result, significant engineering effort is needed to adapt them when new knowledge appears or there are changes in the information needs. This research investigates the usage of knowledge within text classification from two different perspectives. On one hand, the application of descriptive approaches for the seamless modelling of text classification, focusing on knowledge integration and complex data representation. The main goal is to achieve a scalable and efficient approach for rapid prototyping for Text Classification that can incorporate different sources and types of knowledge, and to minimise the gap between the mathematical definition and the modelling of a solution. On the other hand, the improvement of different steps of the classification process where knowledge exploitation has traditionally not been applied. In particular, this thesis introduces two classification sub-tasks, namely Semi-Automatic Text Classification (SATC) and Document Performance Prediction (DPP), and several methods to address them. SATC focuses on selecting the documents that are more likely to be wrongly assigned by the system to be manually classified, while automatically labelling the rest. Document performance prediction estimates the classification quality that will be achieved for a document, given a classifier. In addition, we also propose a family of evaluation metrics to measure degrees of misclassification, and an adaptive variation of k-NN.