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Addis Ababa University Libraries Electronic Thesis and Dissertations: AAU-ETD! >
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Thesis - Information Science >
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http://hdl.handle.net/123456789/1182
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| Title: | DESIGN AND DEVELOPMENT OF AUTOMATIC MORPHOLOGICAL SYNTHESIZER FOR AMHARIC PERFECTIVE VERB FORMS |
| Authors: | KIBUR, LISANU |
| Advisors: | Dr. Yonas Admasu
W/o Woinshet Abdella
Ato Solomon Berhanu |
| Copyright: | 2002 |
| Date Added: | 23-May-2008 |
| Publisher: | Addis Ababa University |
| Abstract: | Natural Language processing plays a significant role in increasing computers’
capability to understand natural languages. Morphological synthesis is one aspect of
the task of understanding natural language, the language by which most human
knowledge is recorded.
Morphological synthesis or generation is a process of returning one or more surface
forms from a sequence of underlying (lexical) forms. Morphological synthesis
systems are used as components in many applications, including machine translation,
spell-check, speech recognition, dictionary (lexicon) compilation, POS tagging,
morphological analysis, conversational systems, automatic sentence construction and
many others.
Today, synthesizers of different kinds have been developed for languages that have
relatively wider use internationally. The same cannot be said for Amharic, the
working language of the Federal Government of Ethiopia, and one of the major
languages of the country (Bender, 1976).
This study is, thus, an attempt to develop a prototype automatic morphological
synthesizer for Amharic, specifically for perfective verb forms.
In this study, algorithms that take into consideration the morphological properties of
Amharic are developed from scratch and applied, as there are no previous such
attempts.
The study adopts the combination of rule-based and neural network approaches to
design and develop a prototype, referred as AmharicMorphologicalSynthesizer. The
rule-based approach generates all the roots successfully where as the neural network
predicts the type of roots in the test data set with an accuracy of 81.48%. For a new
case consisting of 14 roots the neural network identifies type A perfective verb forms
with an accuracy of 80%, type B perfective verb forms with accuracy of 25% and that
of type C perfective verb forms with an accuracy of 100%.
The thesis, in short, describes processes of automated morphological synthesis from
manually synthesizing words to developing a prototype and conducting an experiment
with it. The result obtained using the small manually constructed root table will
encourage the undertaking of further research in the area, especially with the aim of
developing a full-fledged Amharic morphological synthesizer. |
| Description: | A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR
THE DEGREE OF MASTERS OF SCIENCE IN INFORMATION SCIENCE |
| URI: | http://hdl.handle.net/123456789/1182 |
| Appears in: | Thesis - Information Science
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