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' / 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%.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12607922/index.pdf |
| Date | 01 December 2006 |
| Creators | Coltekin, Cagri |
| Contributors | Bozsahin, Cem H |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | - M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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