Thesis (Ph. D.)--University of California, Santa Cruz, 2001. / Typescript. Includes bibliographical references (leaves 273-287).

Thesis (Ph. D.)--University of California, Santa Cruz, 1994. / Typescript. Includes bibliographical references (leaves 157-162).

Thesis (Ph. D.)--University of California, Santa Cruz, 1997. / Typescript. Includes bibliographical references (leaves 223-229).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1968. / Typescript. Vita. Description based on print version record. Includes bibliographical references (leaves 65-68).

Thesis (Ph. D.)--University of Texas, Austin, 1983. / Vita. Description based on print version record. Includes bibliographical references (leaves 145-147).

M.A. (African Languages) / The main purpose of this study was to investigate morphological reduplication in the word class nouns, verbs and ideophones in Zulu, and to describe them according to their formal and semantic features. An outline is given in chapter 1 of the status of the morpheme within the word morphology according to general linguistic principles, theories concerning the morpheme as well as the character or nature of morphological reduplication. According to the literature on this subject it appears that morphological reduplication performs widely, even universally. In chapter 2 attention is given to reduplication in the most important languages and dialects in the Nguni language group apart from Zulu. These languages and dialects include the following: Swati, Bhaca, Phuthi, Northen Transvaal Ndebele, Lala, Xhosa, Southern Transvaal Ndebele and Mpondo. It is found that all these languages and dialects present a common character with regard to morphological reduplication. Zulu, as the main concern of study in respect of morphological reduplication, is studied in detail in chapter 3. The word classes of Zulu are looked at according to Van Wyk's word class grouping, whilst special attention is given to nouns. It is found that morphological reduplication in Zulu nouns shows an extention of the semantic features represented in the single noun. The reduplicated forms have the semantic features of especially abundance, numerousness and completeness. The formal features of the reduplicated noun in Zulu are studied with special reference to the monosyllabic noun, the disyllabic noun and the polisyllabic noun. Their possible combination (in their reduplicated form) with a suffix as well as the formal features of the reduplicated suffix, are studied at length. In chapter 4 the morphological reduplication of verbs (in this instance also monosyllabic, disyllabic and polisyllabic stems) is discussed. The verb can be reduplicated as a whole or only partially with the semantic features of, inter alia, intensity or thoroughness. The verb can also be reduplicated in combination with a suffix with the result that the semantic feature of the suffix acts complementary to the semantic features of the reduplicated verb. In chapter 5 the morphological reduplication of ideophones is discussed. The semantic features of reduplication are characterised as intense, complete, purposeful and final. The formal features show that only disyllabic ideophones represent morphological reduplication that may be fully or only partially reduplicated.

Statistical learning offers a trove of opportunities for problems where a large amount of data is available but falls short when data are limited. For example, in medicine, statistical learning has been used to outperform dermatologists in diagnosing melanoma visually from millions of photos of skin lesions. However, many other medical applications of this kind of learning are made impossible due to the lack of sufficient learning data, for example, performing similar diagnosis of soft tissue tumors within the body based on radiological imagery of blood vessel development. A key challenge underlying this situation is that many statistical learning approaches utilize unstructured data representations such as strings of text or raw images, that don’t intrinsically incorporate structural information. Shape grammar is a way of using visual rules to define the underlying structure of geometric data, pioneered by the design community. Shape grammar rules are replacement rules in which the left side of the rule is a search pattern and the right side is a replacement pattern which can replace the left side where it is found. Traditionally shape grammars have been assembled by hand through observation, making it slow to use them and limiting their use with complex data. This work introduces a way to automate the generation of shape grammars and a technique to use grammars for classification in situations with limited data. A method for automatically inducing grammars from graph based data using a simple recursive algorithm, providing non-probabilistic rulesets, is introduced. The algorithm uses iterative data segmentation to establish multi scale shape rules, and can do so with a single dataset. Additionally, this automatic grammar induction algorithm has been extended to apply to high dimensional data in a nonvisual domain, for example, graphs like social networks. We validated our method by comparing our results to grammars made of historic buildings and products and found it performed comparably grammars made by humans. The induction method was extended by introducing a classification approach based on mapping grammar rule occurrences to dimensions in a high dimensional vector space. With this representation data samples can be analyzed and quickly classified, without the need for data intensive statistical learning. We validated this method by performing sensitivity tests on key graph augmentations and found that our method was comparably sensitive and significantly faster at learning than related existing methods at detecting graph differences across cases. The automated grammar technique and the grammar based classification technique were used together to classify magnetic resonance imaging (MRI) of the brain of 17 individuals and showed that our methods could detect a variety of vasculature borne condition indicators with short and long-term health implications. Through this study we demonstrate that automated grammar based representations can be used for efficient classification of anomalies in abstract domains such as design and biological tissue analysis.

Implementations of grammatical theory have traditionally been based upon Context- Free Grammar (CFG) formalisms which all but ignore questions of learnability. Even implementations which are based upon theories of Generative Grammar (GG), a paradigm which is supposedly motivated by learnability, rarely address such questions. In this thesis we will examine a GG theory which has been formulated primarily to address questions of learnability and present an implementation based upon this theory. The theory argues from Chomsky's definition of epistemological priority that principles which match elements and structures from prelinguistic systems with elements and structures in linguistic systems are preferable to those which are defined purely linguistically or non-linguistically. A procedure for constructing phrase-structure representations from prelinguistic relations using principles of node percolation (rather than the traditional X-theory of GG theories or phrase-structure rules of CFG theories) is presented and this procedure integrated into a left-right, primarily bottom-up parsing mechanism. Specifically, we present a parsing mechanism which derives phrase-structure representations of sentences from Case- and 0-relations using a small number of Percolation Principles. These Percolation Principles simply determine the categorial features of the dominant node of any two adjacent nodes in a representational tree, doing away with explicit phrase structure rules altogether. The parsing mechanism also instantiates appropriate empty categories using a filler-driven paradigm for leftward argument and non-argument movement. Procedures modelling learnability are not implemented in this work, but the applicability of the presented model to a computational model of language is discussed. / Science, Faculty of / Computer Science, Department of / Graduate

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