Thesis (MSc)--Stellenbosch University , 2002. / ENGLISH ABSTRACT: Grammars with regulated rewriting are used to restrict the application of contextfree productions in order to avoid certain derivations. This enables these grammars to generate both context-free and non-context-free languages using only production rules with a context-free format. These grammars are more powerful than contextfree grammars, but usually not as powerful as context-sensitive grammars. Various grammars with regulated rewriting have been developed and some will be discussed in this thesis. Propositional linear temporal logic is a formal system used to describe truth values of propositions over time. This is done by defining a timeline together with a set of propositions. It is then possible to construct temporal logic formulae, consisting of these propositions and temporal operators, to specify the truth values of the propositions for every step in the timeline. In this thesis we define and discuss temporal grammars that combine grammars with propositionallinear temporal logic. Since a derivation can be associated with a timeline, a regulating device can be constructed from temporal logic formulae, that will control the application of productions within the derivation. The discussion on temporal grammars includes some of the properties of these grammars, while many ideas are illustrated by examples. / AFRIKAANSE OPSOMMING: Grammatikas met gereguleerde herskrywing word gebruik om 'n beperking te plaas op die toepassing van konteksvrye produksies en verhoed sodoende sekere afleidings. Hierdie grammatikas beskik oor die vermoe om beide konteksvrye en nie-konteksvrye tale te genereer deur slegs produksiereels van 'n konteksvrye formaat te gebruik. Grammatikas met gereguleerde herskrywing is dus sterker as konteksvrye grammatikas, alhoewel dit soms swakker as konteks-sensitiewe grammatikas is. 'n Verskeidenheid sulke grammatikas is al ontwikkel en sommige sal in hierdie tesis bespreek word. Proposisionele lineere temporale logika is 'n formele stelsel wat gebruik kan word om die waarheidswaardes van proposisies oor tyd te beskryf. Dit word gedoen deur 'n tydlyn, asook 'n versameling proposisies te definieer. Dit is clan moontlik om temporale operatore tesame met die proposisies te gebruik om temporale logika-formules te konstrueer wat in staat is om waarheidswaardes van die proposisies te spesifiseer vir elke oomblik in die tydlyn. In hierdie tesis word temporale grammatikas, wat grammatikas met proposisionele lineere temporale logika kombineer, gedefinieer en bespreek. Aangesien 'n afleiding met 'n tydlyn geassosieer kan word, is dit moontlik om 'n regulerende meganisme uit temporale logika-formules te konstrueer wat die toepassing van produksiereels in die afleiding kontroleer. Die bespreking van temporale grammatikas sluit 'n verskeidenheid eienskappe van die grammatikas in, asook 'n aantal voorbeelde wat ter illustrasie gebruik word.

Syntactic parsing is one of the best understood language processing applications. Since language and grammar have been formally defined, it is easy for computers to parse the syntactic structure of natural language text. Does meaning have structure as well? If it has, how can we analyze the structure? Previous systems rely on a one-to-one correspondence between syntactic rules and semantic rules. But such systems can only be applied to limited fragments of English. In this thesis, we propose a general-purpose shallow semantic parser which utilizes a semantic network (WordNet), and a frame dataset (FrameNet). Semantic relations recognized by the parser are based on how human beings represent knowledge of the world. Parsing semantic structure allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.

Call number: LD2668 .T4 1986 L364 / Master of Science / Computing and Information Sciences

Abstract not available

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Computer Science, 2009. / Includes bibliographical references.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Computer Science, 2009. / Includes bibliographical references.

Indiana University-Purdue University Indianapolis (IUPUI) / Recent technological applications have made the life of people too much dependent on Science, Technology, Engineering, and Mathematics (STEM) and its applications. Understanding basic level science is a must in order to use and contribute to this technological revolution. Science education in middle and high school levels however depends heavily on visual representations such as models, diagrams, figures, animations and presentations etc. This leaves visually impaired students with very few options to learn science and secure a career in STEM related areas. Recent experiments have shown that small aural clues called Audemes are helpful in understanding and memorization of science concepts among visually impaired students. Audemes are non-verbal sound translations of a science concept. In order to facilitate science concepts as Audemes, for visually impaired students, this thesis presents an automatic system for audeme generation from STEM textbooks. This thesis describes the systematic application of multiple Natural Language Processing tools and techniques, such as dependency parser, POS tagger, Information Retrieval algorithm, Semantic mapping of aural words, machine learning etc., to transform the science concept into a combination of atomic-sounds, thus forming an audeme. We present a rule based classification method for all STEM related concepts. This work also presents a novel way of mapping and extracting most related sounds for the words being used in textbook. Additionally, machine learning methods are used in the system to guarantee the customization of output according to a user's perception. The system being presented is robust, scalable, fully automatic and dynamically adaptable for audeme generation.