Lo, Ka Kan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 97-104). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.3 / Chapter 1.2 --- New paradigm in language learning --- p.5 / Chapter 1.3 --- Semantic Relations --- p.7 / Chapter 1.4 --- Contribution of this thesis --- p.9 / Chapter 2 --- Related Work --- p.13 / Chapter 2.1 --- Theoretical Linguistics --- p.13 / Chapter 2.1.1 --- Overview --- p.13 / Chapter 2.1.2 --- Analysis --- p.15 / Chapter 2.2 --- Computational Linguistics - General Learning --- p.17 / Chapter 2.3 --- Computational Linguistics - HPSG Lexical Acquisition --- p.20 / Chapter 2.4 --- Learning approach --- p.22 / Chapter 3 --- Background --- p.25 / Chapter 3.1 --- Modeling primitives --- p.26 / Chapter 3.1.1 --- Feature Structure --- p.26 / Chapter 3.1.2 --- Word --- p.28 / Chapter 3.1.3 --- Phrase --- p.35 / Chapter 3.1.4 --- Clause --- p.36 / Chapter 3.2 --- Wikipedia Resource --- p.38 / Chapter 3.2.1 --- Encyclopedia Text --- p.40 / Chapter 3.3 --- Semantic Relations --- p.40 / Chapter 4 --- Learning Framework - Syntactic and Semantic --- p.46 / Chapter 4.1 --- Type feature scoring function --- p.48 / Chapter 4.2 --- Confidence score of lexical entry --- p.50 / Chapter 4.3 --- Specialization and Generalization --- p.52 / Chapter 4.3.1 --- Further Processing --- p.54 / Chapter 4.3.2 --- Algorithm Outline --- p.54 / Chapter 4.3.3 --- Algorithm Analysis --- p.55 / Chapter 4.4 --- Semantic Information --- p.57 / Chapter 4.4.1 --- Extraction --- p.58 / Chapter 4.4.2 --- Induction --- p.60 / Chapter 4.4.3 --- Generalization --- p.63 / Chapter 4.5 --- Extension with new text documents --- p.65 / Chapter 4.6 --- Integrating the syntactic and semantic acquisition framework --- p.65 / Chapter 5 --- Evaluation --- p.68 / Chapter 5.1 --- Evaluation Metric - English Resource Grammar --- p.68 / Chapter 5.1.1 --- English Resource Grammar --- p.69 / Chapter 5.2 --- Experiments --- p.71 / Chapter 5.2.1 --- Tasks --- p.71 / Chapter 5.2.2 --- Evaluation Measures --- p.77 / Chapter 5.2.3 --- Methodologies --- p.78 / Chapter 5.2.4 --- Corpus Preparation --- p.79 / Chapter 5.2.5 --- Results --- p.81 / Chapter 5.3 --- Result Analysis --- p.85 / Chapter 6 --- Conclusions --- p.95 / Bibliography --- p.97

Thesis. 1978. Ph.D.--Massachusetts Institute of Technology. Dept. of Psychology. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND HUMANITIES. / Vita. / Bibliography: leaves 112-118. / Ph.D.

No description available.

Mo, Deyuan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 153-167). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese; appendixes includes Chinese.

A vast array of computer-based simulation tools are used to support engineering design and analysis activities. Several such activities call for the simulation of various coupled sub-systems in parallel, typically to study the emergent behavior of large, complex systems. Most sub-systems have their own simulation models associated with them, which need to interoperate with each other in a federated fashion to simulate system-level behavior. The run-time exchange of information between federate simulations requires a common information model that defines the representation of simulation concepts shared between federates. However, most federate simulations employ disparate representations of shared concepts. Therefore, it is often necessary to implement transformation stubs that convert concepts between their common representation to those used in federate simulations. The tasks of defining a common representation for shared simulation concepts and building translation stubs around them adds to the cost of performing a system-level simulation. In this thesis, a framework to support automation and reuse in the process of achieving interoperability between federate simulations is developed. This framework uses ontologies as a means to capture the semantics of different simulation concepts shared in a federation in a formal, reusable fashion. Using these semantics, a common representation for shared simulation entities, and a corresponding set of transformation stubs to convert entities from their federate to common representations (and vice-versa) are derived automatically. As a foundation to this framework, a schema to enable the capture of simulation concepts in an ontology is specified. Also, a graph-based algorithm is developed to extract the appropriate common information model and transformation procedures between federate and common simulation entities. As a proof of concept, this framework is applied to support the development of a federated air traffic simulation. To progress with the design of an airport, the combined operation of its individual systems (air traffic control, ground traffic control, and ground-based aircraft services) in handling varying volumes of aircraft traffic is to be studied. To do so, the individual simulation models corresponding to the different sub-systems of the airport need to be federated, for which the ontology-based framework is applied.

This work investigates the effective acquisition of lexical knowledge from the Web to perform semantic interpretation. The Web provides an unprecedented amount of natural language from which to gain knowledge useful for semantic interpretation. The knowledge acquired is described as common sense knowledge, information one uses in his or her daily life to understand language and perception. Novel approaches are presented for both the acquisition of this knowledge and use of the knowledge in semantic interpretation algorithms. The goal is to increase accuracy over other automatic semantic interpretation systems, and in turn enable stronger real world applications such as machine translation, advanced Web search, sentiment analysis, and question answering. The major contributions of this dissertation consist of two methods of acquiring lexical knowledge from the Web, namely a database of common sense knowledge and Web selectors. The first method is a framework for acquiring a database of concept relationships. To acquire this knowledge, relationships between nouns are found on the Web and analyzed over WordNet using information-theory, producing information about concepts rather than ambiguous words. For the second contribution, words called Web selectors are retrieved which take the place of an instance of a target word in its local context. The selectors serve for the system to learn the types of concepts that the sense of a target word should be similar. Web selectors are acquired dynamically as part of a semantic interpretation algorithm, while the relationships in the database are useful to iii stand-alone programs. A final contribution of this dissertation concerns a novel semantic similarity measure and an evaluation of similarity and relatedness measures on tasks of concept similarity. Such tasks are useful when applying acquired knowledge to semantic interpretation. Applications to word sense disambiguation, an aspect of semantic interpretation, are used to evaluate the contributions. Disambiguation systems which utilize semantically annotated training data are considered supervised. The algorithms of this dissertation are considered minimallysupervised; they do not require training data created by humans, though they may use humancreated data sources. In the case of evaluating a database of common sense knowledge, integrating the knowledge into an existing minimally-supervised disambiguation system significantly improved results – a 20.5% error reduction. Similarly, the Web selectors disambiguation system, which acquires knowledge directly as part of the algorithm, achieved results comparable with top minimally-supervised systems, an F-score of 80.2% on a standard noun disambiguation task. This work enables the study of many subsequent related tasks for improving semantic interpretation and its application to real-world technologies. Other aspects of semantic interpretation, such as semantic role labeling could utilize the same methods presented here for word sense disambiguation. As the Web continues to grow, the capabilities of the systems in this dissertation are expected to increase. Although the Web selectors system achieves great results, a study in this dissertation shows likely improvements from acquiring more data. Furthermore, the methods for acquiring a database of common sense knowledge could be applied in a more exhaustive fashion for other types of common sense knowledge. Finally, perhaps the greatest benefits from this work will come from the enabling of real world technologies that utilize semantic interpretation.