Approved for Public Release; distribution is unlimited / Formal methods hold significant potential for automating the development, refinement, and implementation of policy. For this potential to be realized, however, improved techniques are required for converting natural-language statements of policy into a computational form. In this paper we present and analyze an architecture for carrying out this conversion. The architecture employs semantic networks to represent both policy statements and objects in the domain of those statements. We present a case study which illustrates how a system based on this architecture could be developed. The case study consists of an analysis of natural language policy statements taken from a policy document for web sites at a university, and is carried out with support from a software tool we developed which converts text output from a natural language parser into a graphical form. / Naval Postgraduate School author (civilian).

Mok, Oi Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 94-99). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Thesis Goals --- p.3 / Chapter 1.3 --- Thesis Outline --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Natural Language Understanding --- p.5 / Chapter 2.1.1 --- Rule-based Approaches --- p.7 / Chapter 2.1.2 --- Phrase-spotting Approaches --- p.8 / Chapter 2.1.3 --- Stochastic Approaches --- p.9 / Chapter 2.2 --- Belief Network Framework - the N Binary Formulation --- p.11 / Chapter 2.2.1 --- Introduction of Belief Network --- p.11 / Chapter 2.2.2 --- The N Binary Formulation --- p.13 / Chapter 2.2.3 --- Semantic Tagging --- p.13 / Chapter 2.2.4 --- Belief Networks Development --- p.14 / Chapter 2.2.5 --- Goal Inference --- p.15 / Chapter 2.2.6 --- Potential Problems --- p.16 / Chapter 2.3 --- The ATIS Domain --- p.17 / Chapter 2.4 --- Chapter Summary --- p.19 / Chapter 3 --- Belief Network Framework - the One N-ary Formulation --- p.21 / Chapter 3.1 --- The One N-ary Formulation --- p.22 / Chapter 3.2 --- Belief Network Development --- p.23 / Chapter 3.3 --- Goal Inference --- p.24 / Chapter 3.3.1 --- Multiple Selection Strategy --- p.25 / Chapter 3.3.2 --- Maximum Selection Strategy --- p.26 / Chapter 3.4 --- Advantages of the One N-ary Formulation --- p.27 / Chapter 3.5 --- Chapter Summary --- p.29 / Chapter 4 --- Evaluation on the N Binary and the One N-ary Formula- tions --- p.30 / Chapter 4.1 --- Evaluation Metrics --- p.31 / Chapter 4.1.1 --- Accuracy Measure --- p.32 / Chapter 4.1.2 --- Macro-Averaging --- p.32 / Chapter 4.1.3 --- Micro-Averaging --- p.35 / Chapter 4.2 --- Experiments --- p.35 / Chapter 4.2.1 --- Network Dimensions --- p.38 / Chapter 4.2.2 --- Thresholds --- p.39 / Chapter 4.2.3 --- Overall Goal Identification --- p.43 / Chapter 4.2.4 --- Out-Of-Domain Rejection --- p.65 / Chapter 4.2.5 --- Multiple Goal Identification --- p.67 / Chapter 4.2.6 --- Computation --- p.68 / Chapter 4.3 --- Chapter Summary --- p.70 / Chapter 5 --- Portability to Chinese --- p.72 / Chapter 5.1 --- The Chinese ATIS Domain --- p.72 / Chapter 5.1.1 --- Word Tokenization and Parsing --- p.73 / Chapter 5.2 --- Experiments --- p.74 / Chapter 5.2.1 --- Network Dimension --- p.76 / Chapter 5.2.2 --- Overall Goal Identification --- p.77 / Chapter 5.2.3 --- Out-Of-Domain Rejection --- p.83 / Chapter 5.2.4 --- Multiple Goal Identification --- p.86 / Chapter 5.3 --- Chapter Summary --- p.88 / Chapter 6 --- Conclusions --- p.39 / Chapter 6.1 --- Summary --- p.89 / Chapter 6.2 --- Contributions --- p.91 / Chapter 6.3 --- Future Work --- p.92 / Bibliography --- p.94 / Chapter A --- The Communicative Goals --- p.100 / Chapter B --- Distribution of the Communicative Goals --- p.101 / Chapter C --- The Hand-Designed Grammar Rules --- p.103 / Chapter D --- The Selected Concepts for each Belief Network --- p.115 / Chapter E --- The Recalls and Precisions of the Goal Identifiers in Macro- Averaging --- p.125

Yip Wing Lin Winnie. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 102-105). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Thesis Goals --- p.3 / Chapter 1.3 --- Thesis Outline --- p.5 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Natural Language Generation --- p.6 / Chapter 2.1.1 --- Template-based Approach --- p.7 / Chapter 2.1.2 --- Rule-based Approach --- p.8 / Chapter 2.1.3 --- Statistical Approach --- p.9 / Chapter 2.1.4 --- Hybrid Approach --- p.10 / Chapter 2.1.5 --- Machine Learning Approach --- p.11 / Chapter 2.2 --- Evaluation Method --- p.12 / Chapter 2.2.1 --- Cooperative Principles --- p.13 / Chapter 2.3 --- Chapter Summary --- p.13 / Chapter 3 --- Natural Language Understanding --- p.14 / Chapter 3.1 --- The CUHK Restaurant Domain --- p.15 / Chapter 3.2 --- "Task Goals, Dialog Acts, Concept Categories and Annotation" --- p.17 / Chapter 3.2.1 --- Task Goals (TGs) and Dialog Acts (DAs) --- p.17 / Chapter 3.2.2 --- Concept Categories (CTG/CDA) --- p.20 / Chapter 3.2.3 --- Utterance Segmentation and Annotation --- p.21 / Chapter 3.3 --- Task Goal and Dialog Act Identification --- p.22 / Chapter 3.3.1 --- Belief Networks Development --- p.22 / Chapter 3.3.2 --- Task Goal and Dialog Act Inference --- p.24 / Chapter 3.3.3 --- Network Dimensions --- p.25 / Chapter 3.4 --- Chapter Summary --- p.29 / Chapter 4 --- Automatic Utterance Segmentation --- p.30 / Chapter 4.1 --- Utterance Definition --- p.31 / Chapter 4.2 --- Segmentation Procedure --- p.33 / Chapter 4.2.1 --- Tokenization --- p.35 / Chapter 4.2.2 --- POS Tagging --- p.36 / Chapter 4.2.3 --- Multi-Parser Architecture (MPA) Language Parsing --- p.38 / Chapter 4.2.4 --- Top-down Generalized Representation --- p.40 / Chapter 4.3 --- Evaluation --- p.47 / Chapter 4.3.1 --- Results --- p.47 / Chapter 4.3.2 --- Analysis --- p.48 / Chapter 4.4 --- Chapter Summary --- p.50 / Chapter 5 --- Natural Language Response Generation --- p.52 / Chapter 5.1 --- System Overview --- p.52 / Chapter 5.2 --- Corpus-derived Dialog State Transition Rules --- p.55 / Chapter 5.3 --- Hand-designed Text Generation Templates --- p.56 / Chapter 5.4 --- Performance Evaluation --- p.59 / Chapter 5.4.1 --- Task Completion Rate --- p.61 / Chapter 5.4.2 --- Grice's Maxims and Perceived User Satisfaction --- p.62 / Chapter 5.4.3 --- Error Analysis --- p.64 / Chapter 5.5 --- Chapter Summary --- p.65 / Chapter 6 --- Bilingual Response Generation using Semi-Automatically- Induced Response Templates --- p.67 / Chapter 6.1 --- Response Data --- p.68 / Chapter 6.2 --- Semi-Automatic Grammar Induction --- p.69 / Chapter 6.2.1 --- Agglomerative Clustering --- p.69 / Chapter 6.2.2 --- Parameters Selection --- p.70 / Chapter 6.3 --- Application to Response Grammar Induction --- p.71 / Chapter 6.3.1 --- Parameters Selection --- p.73 / Chapter 6.3.2 --- Unsupervised Grammar Induction --- p.76 / Chapter 6.3.3 --- Post-processing --- p.80 / Chapter 6.3.4 --- Prior Knowledge Injection --- p.82 / Chapter 6.4 --- Response Templates Generation --- p.84 / Chapter 6.4.1 --- Induced Response Grammar --- p.84 / Chapter 6.4.2 --- Template Formation --- p.84 / Chapter 6.4.3 --- Bilingual Response Templates --- p.89 / Chapter 6.5 --- Evaluation --- p.89 / Chapter 6.5.1 --- "Task Completion Rate, Grice's Maxims and User Sat- isfaction" --- p.91 / Chapter 6.6 --- Chapter Summary --- p.94 / Chapter 7 --- Conclusion --- p.96 / Chapter 7.1 --- Summary --- p.96 / Chapter 7.2 --- Contributions --- p.98 / Chapter 7.3 --- Future Work --- p.100 / Bibliography --- p.102 / Chapter A --- Domain-Specific Task Goals in the CUHK Restaurants Do- main --- p.107 / Chapter B --- Full List of VERBMOBIL-2 Dialog Acts --- p.109 / Chapter C --- Dialog Acts for Customer Requests and Waiter Responsesin the CUHK Restaurants Domain --- p.111 / Chapter D --- Grammar for Task Goal and Dialog Act Identification --- p.116 / Chapter E --- Utterance Definition --- p.119 / Chapter F --- Dialog State Transition Rules --- p.121 / Chapter G --- Full List of Templates Selection Conditions --- p.125 / Chapter H --- Hand-designed Text Generation Templates --- p.130 / Chapter I --- Evaluation Test Questionnaire for Dialog System in the CUHK Restaurant Domain --- p.135 / Chapter J --- POS Tags --- p.137 / Chapter K --- Full List of Lexicon and contextual rule modifications --- p.139 / Chapter L --- Top-down Generalized Representations --- p.141 / Chapter M --- Sample Outputs for Automatic Utterance Segmentation --- p.144 / Chapter N --- Induced Grammar --- p.145 / Chapter O --- Seeded Categories --- p.148 / Chapter P --- Semi-Automatically-Induced Response Templates --- p.150 / Chapter Q --- Details of the Statistical Testing Regarding Grice's Maxims and User Satisfaction --- p.156

Thesis (M. Phil.)--University of Hong Kong, 1989.

Thesis (M.S. in Software Engineering)--Naval Postgraduate School, March 2003. / Thesis advisor(s): James Bret Michael, Neil C. Rowe. Includes bibliographical references (p. 91-92). Also available online.

published_or_final_version / Computer Science / Master / Master of Philosophy

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 143-146). Also available via the Internet.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, June 1990. / Thesis Advisor(s): Lum, Vincent Y. ; Rowe, Neil C. "June 1990." Description based on signature page. DTIC Identifiers: Interfaces, natural language, databases, theses. Author(s) subject terms: Natural language processing, multimedia database system, natural language interface, descriptive captions. Includes bibliographical references (p. 27).

Thesis (B.A.)--Haverford College, Dept. of Computer Science, Swarthmore College. Dept. of Linguistics, 2007. / Includes bibliographical references.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisors: Kathleen F. McCoy and Vijay K. Shanker, Computer & Information Sciences. Includes bibliographical references.