Tsui Wai-Ching. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 115-122). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Natural Language Understanding Using Belief Networks --- p.5 / Chapter 1.3 --- Integrating Speech Recognition with Natural Language Un- derstanding --- p.7 / Chapter 1.4 --- Thesis Goals --- p.9 / Chapter 1.5 --- Thesis Organization --- p.10 / Chapter 2 --- Background --- p.12 / Chapter 2.1 --- Natural Language Understanding Approaches --- p.13 / Chapter 2.1.1 --- Rule-based Approaches --- p.15 / Chapter 2.1.2 --- Stochastic Approaches --- p.16 / Chapter 2.1.3 --- Mixed Approaches --- p.18 / Chapter 2.2 --- Portability of Natural Language Understanding Frameworks --- p.19 / Chapter 2.2.1 --- Portability across Domains --- p.19 / Chapter 2.2.2 --- Portability across Languages --- p.20 / Chapter 2.2.3 --- Portability across both Domains and Languages --- p.21 / Chapter 2.3 --- Spoken Language Understanding --- p.21 / Chapter 2.3.1 --- Integration of Speech Recognition Confidence into Nat- ural Language Understanding --- p.22 / Chapter 2.3.2 --- Integration of Other Potential Confidence Features into Natural Language Understanding --- p.24 / Chapter 2.4 --- Belief Networks --- p.24 / Chapter 2.4.1 --- Overview --- p.24 / Chapter 2.4.2 --- Bayesian Inference --- p.26 / Chapter 2.5 --- Transformation-based Parsing Technique --- p.27 / Chapter 2.6 --- Chapter Summary --- p.28 / Chapter 3 --- Portability of the Natural Language Understanding Frame- work across Application Domains and Languages --- p.31 / Chapter 3.1 --- Natural Language Understanding Framework --- p.32 / Chapter 3.1.1 --- Semantic Tagging --- p.33 / Chapter 3.1.2 --- Informational Goal Inference with Belief Networks --- p.34 / Chapter 3.2 --- The ISIS Stocks Domain --- p.36 / Chapter 3.3 --- A Unified Framework for English and Chinese --- p.38 / Chapter 3.3.1 --- Semantic Tagging for the ISIS domain --- p.39 / Chapter 3.3.2 --- Transformation-based Parsing --- p.40 / Chapter 3.3.3 --- Informational Goal Inference with Belief Networks for the ISIS domain --- p.43 / Chapter 3.4 --- Experiments --- p.45 / Chapter 3.4.1 --- Goal Identification Experiments --- p.45 / Chapter 3.4.2 --- A Cross-language Experiment --- p.49 / Chapter 3.5 --- Chapter Summary --- p.55 / Chapter 4 --- Enhancement in the Belief Networks for Informational Goal Inference --- p.57 / Chapter 4.1 --- Semantic Concept Selection in Belief Networks --- p.58 / Chapter 4.1.1 --- Selection of Positive Evidence --- p.58 / Chapter 4.1.2 --- Selection of Negative Evidence --- p.62 / Chapter 4.2 --- Estimation of Statistical Probabilities in the Enhanced Belief Networks --- p.64 / Chapter 4.2.1 --- Estimation of Prior Probabilities --- p.65 / Chapter 4.2.2 --- Estimation of Posterior Probabilities --- p.66 / Chapter 4.3 --- Experiments --- p.73 / Chapter 4.3.1 --- Belief Networks Developed with Positive Evidence --- p.74 / Chapter 4.3.2 --- Belief Networks with the Injection of Negative Evidence --- p.76 / Chapter 4.4 --- Chapter Summary --- p.82 / Chapter 5 --- Integration between Speech Recognition and Natural Lan- guage Understanding --- p.84 / Chapter 5.1 --- The Speech Corpus for the Chinese ISIS Stocks Domain --- p.86 / Chapter 5.2 --- Our Extended Natural Language Understanding Framework for Spoken Language Understanding --- p.90 / Chapter 5.2.1 --- Integrated Scoring for Chinese Speech Recognition and Natural Language Understanding --- p.92 / Chapter 5.3 --- Experiments --- p.92 / Chapter 5.3.1 --- Training and Testing on the Perfect Reference Data Sets --- p.93 / Chapter 5.3.2 --- Mismatched Training and Testing Conditions ´ؤ Perfect Reference versus Imperfect Hypotheses --- p.93 / Chapter 5.3.3 --- Comparing Goal Identification between the Use of Single- best versus N-best Recognition Hypotheses --- p.95 / Chapter 5.3.4 --- Integration of Speech Recognition Confidence Scores into Natural Language Understanding --- p.97 / Chapter 5.3.5 --- Feasibility of Our Approach for Spoken Language Un- derstanding --- p.99 / Chapter 5.3.6 --- Justification of Using Max-of-max Classifier in Our Single Goal Identification Scheme --- p.107 / Chapter 5.4 --- Chapter Summary --- p.109 / Chapter 6 --- Conclusions and Future Work --- p.110 / Chapter 6.1 --- Conclusions --- p.110 / Chapter 6.2 --- Contributions --- p.112 / Chapter 6.3 --- Future Work --- p.113 / Bibliography --- p.115 / Chapter A --- Semantic Frames for Chinese --- p.123 / Chapter B --- Semantic Frames for English --- p.127 / Chapter C --- The Concept Set of Positive Evidence for the Nine Goalsin English --- p.131 / Chapter D --- The Concept Set of Positive Evidence for the Ten Goalsin Chinese --- p.133 / Chapter E --- The Complete Concept Set including Both the Positive and Negative Evidence for the Ten Goals in English --- p.135 / Chapter F --- The Complete Concept Set including Both the Positive and Negative Evidence for the Ten Goals in Chinese --- p.138 / Chapter G --- The Assignment of Statistical Probabilities for Each Selected Concept under the Corresponding Goals in Chinese --- p.141 / Chapter H --- The Assignment of Statistical Probabilities for Each Selected Concept under the Corresponding Goals in English --- p.146
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324036 |
| Date | January 2002 |
| Contributors | Tsui, Wai-Ching., Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xvii, 151 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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