This thesis aims to investigate the role of the syllable, a non-meaning bearing unit, in learning high level meaning bearing units---the lexical items of language. A computational model has been developed to learn the meaning bearing units of the language, assuming knowledge of syllables. The input to the system comprises of words marked at syllable boundaries together with their meanings. Using a statistical learning algorithm, the model discovers the meaning bearing elements with their respective syntactic categories. The model&#039 / s success has been tested against a second model that has been trained with the same corpus segmented at morpheme boundaries. The lexicons learned by both models have been found to be similar, with an exact overlap of 71%.

This thesis is a descriptive study of four contrastive discourse connectives in Turkish. The main aim of this study is to analyze the connectives with respect to their meaning and predicate-argument structure and lay out the similarities and differences among contrastive discourse connectives with the help of quantitative analysis. Although the study is limited with contrastive connectives, it will have implications on how to resolve discourse structure in general and illustrate how lexico-syntactic elements contribute to discourse semantics.

Nowadays event extraction systems mainly deal with a relatively small amount of information about temporal and modal qualifications of situations, primarily processing assertive sentences in the past tense. However, systems with a wider coverage of tense, aspect and mood can provide better analyses and can be used in a wider range of text analysis applications. This thesis develops such a system for Turkish language. This is accomplished by extending Open Source Information Mining and Analysis (OPTIMA) research group&#039 / s event extraction software, by implementing appropriate extensions in the semantic representation format, by adding a partial grammar which improves the TAM (Tense, Aspect and Mood) marker, adverb analysis and matching functions of ExPRESS, and by constructing an appropriate lexicon in the standard of CORLEONE. These extensions are based on the theory of anchoring relations (Tem&uuml / rc&uuml / , 2007, 2011) which is a cross-linguistically applicable semantic framework for analyzing tense, aspect and mood related categories. The result is a system which can, in addition to extracting basic event structures, classify sentences given in news reports according to their temporal, modal and volitional/illocutionary values. Although the focus is on news reports of natural disasters, disease outbreaks and man-made disasters in Turkish language, the approach can be adapted to other languages, domains and genres. This event extraction and classification system, with further developments, can provide a basis for automated browsing systems for preventing environmental and humanitarian risk.