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 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%.