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).

Many systems of Knowledge Representation exist, but none were designed specifically for general purpose large scale natural language processing. This thesis introduces a set of metrics to evaluate the suitability of representations for this purpose, derived from an analysis of the problems such processing introduces. These metrics address three broad categories of question: Is the representation sufficiently expressive to perform its task? What implications has its design on the architecture of the system using it? What inefficiencies are intrinsic to its design? An evaluation of existing Knowledge Representation systems reveals that none of them satisfies the needs of general purpose large scale natural language processing. To remedy this lack, this thesis develops a new representation: SemNet. SemNet benefits not only from the detailed requirements analysis but also from insights gained from its use as the core representation of the large scale general purpose system LOLITA (Large-scale Object-based Linguistic Interactor, Translator, and Analyser). The mapping process between Natural language and representation is presented in detail, showing that the representation achieves its goals in practice.

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 (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xv, 268 p.; also includes graphics. Includes abstract and vita. Advisor: Chris Brew, Dept. of Linguistics. Includes bibliographical references (p. 252-266) and index.

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.

Why do some groups of people work well together while others do not? It is commonly accepted that effective groups communicate well. Yet one of the biggest roadblocks facing the study of group communication is that it is extremely difficult to capture real-world group interactions and analyze the words people use in a timely manner. This project overcame this limitation in two ways. First, a broader and more systematic study of group processes was conducted by using a computerized text analysis program (Linguistic Inquiry and Word Count) that automatically codes natural language using pre-established rules. Groups that work well together typically exchange more knowledge and establish good social relationships, which is reflected in the way that they use words. The group dynamics of over 500 student discussion groups interacting via group chat were assessed by studying their language use. Second, a language feedback system was built to experimentally test the importance of certain group processes on group satisfaction and performance. It is now possible to provide language feedback by processing natural language dialogue using computerized text analysis in real time. The language feedback system can change the way the group works by providing individualized recommendations. In this way it is possible to manipulate group processes naturalistically. Together these studies provided evidence that important group processes can be detected even using simplistic natural language processing, and preliminary evidence that providing real-time feedback based on the words students use in a group discussion can improve learning by changing how the group works together. / text

published_or_final_version / Computer Science / Master / Master of Philosophy

Name Search is an important search function in various types of information retrieval systems, such as online library catalogs and electronic yellow pages. It is also difficult due to the high degree of fuzziness required in matching name variants. Previous approaches to name search systems use ad hoc combinations of search heuristics. This paper first discusses two approaches to name modelingâ the natural language processing (NLP) and the information retrieval (IR) modelsâ and proposes a hybrid approach. The approach demonstrates a critical combination of complementary NLP and IR features that produces more effective fuzzy name matching. Two principles, position-as-attribute and position-transitionlikelihood, are introduced as the principles for integrating the advantageous aspects of both approaches. They have been implemented in an NLP- and IR- hybrid model system called Friendly Name Search (FNS) for real world applications in multilingual directory searches on the Singapore Yellow pages website.