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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1092

Title: AN AUTOMATIC SENTENCE PARSER FOR OROMO LANGUAGE USING SUPERVISED LEARNING TECHNIQUE
Authors: DIRIBA, MEGERSA
Advisors: Ato Mesfin Getachew
Ato Million Meshesha
Dr. Haile Eyesus Engdashet
Copyright: 2002
Date Added: 14-May-2008
Publisher: Addis Ababa University
Abstract: The goal of Information Retrieval has been to reduce human language complexities and as a result serve users in the most efficient way. The decisive tool in achieving such end is the Natural language Processing (NLP). NLP has many components in serving such purpose. Parsing is one of such components in NLP in improving precision and recall which is the goal of Information Retrieval Systems. Moreover, parsing is also used in the effort towards machine translation which is one of the heart of Natural Language Processing. Today, different kinds of parsers have been developed for languages, which have relatively wider use nationally and/or internationally since the 1960s. Unfortunately Oromo has not captured the advantage of such system being the working language of the State Government of Oromiya, and one of the major languages in Ethiopia and Africa (Abebe 2002) for there are no systems (parsers of any sort) that parse written texts in this language. This study is, therefore, an attempt to develop a simple automatic sentence parser for Oromo language. In the study, the chart algorithm was used with some modification. A module for morphological analyzer, which splits words into root form and their corresponding morpheme, was also developed in order to facilitate the preparation of texts in a file to be parsed with appropriate lexical categories. In addition, the unsupervised learning algorithm was designed to guide the parser in predicting unknown and ambiguous words in a sentence. Grammar rules, lexicon, morphological rules and contextual information were also designed on the basis of the review made on the linguistic properties of Oromo grammatical categories. This system, in fact, is the first in its kind for this language.
Description: A thesis submitted to the school of Graduate Studies of Addis Ababa University in Partial fulfillment for the Degree of Master of Science in Information Science
URI: http://hdl.handle.net/123456789/1092
Appears in:Thesis - Information Science

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