Browsing by Author "Megersa, Diriba"

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    An Automatic Sentence Parser for Oromo Language using Supervised Learning Technique
    (Addis Ababa University, 2002-06) Megersa, Diriba; Getachew, Mesfin; Meshesha, Million; Engdashet, Haile Eyesus
    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. The study adopts an intelligent (Rule-Based+ learning module) approach to develop a prototype, which is a simple Oromo parser for the language. The thesis, in short, describes processes of automated sentence parsing of Free Texts. That is, it is aimed at developing a prototype and conducting an experiment with it. The result obtained (95% on the training test and 88.5% on the test set) using the small manually parsed sentences encourage further research to be launched, especially with the aim of developing a full-fledged Oromo sentence parser.
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    An Automatic Sentence Parser for Oromo Language Using Supervised Learning Technique
    (Addis Ababa University, 2002-06) Megersa, Diriba; Getachew, Mesfin (PhD); Meshesha, Million (PhD)
    The goal of Informal ion Retrieval has been to reduce human language complexities and as a result serve users in The mos I efficient way. The decisive 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 calligraphic is The goal of Informal ion Retrieval Systems. Moreover, parsing is also used inhere{for warlords machine Translation which is one of the hear of Natural Language Processing. Today, difference kinds of parsers have been developed' languages. lhis hare relatively wider use nationally and/or international/ly since The 1960.1. Un[unalterably Gromo has nol captured Ihe advanlage of such .Iyslem being Ihe working language of Ihe Slale Government of Gromiya, and one of Ihe major languages in Elhiopia and Ababa (Abebe 2002) lor Ihere are no syslems (parsers of any sarI) Ihal parse wril/en lexlS in Ihis language. This siudy is, Iherefore, an allempl 10 develop a simple aulomalic .lenIence parser for Oromo language In Ihe sludy, Ihe chari algorilhm 11 '0.1 used lI'ilh some modi/iealion. A module (or mOlphological analyzer, which splils words inlo roOI form and Iheir wrresponding morpheme, was also developed in order 10 faeil ilale Ihe preparalion of lexls in a lile 10 be parsed wilh appropriale lexical calegories. In addition, The unsupervised learning algorilhm was designed 10 guide The parser in predicting unknown and ambiguous words in a sentence. Grammar rules, lexicon, morphological rules and lexicon in-formalin were also designed on The basis of Ihe review Decide on Ihe linguistic propellers of amII/o grumll1alical categories. This system, facing, is the firslinils kind fiJI' this language. The study adopts an intelligent (Rule-Based+ learning Inodule) approach to develop a prototype. which is a simple Drama parser/or the language. The thesis. in short. describes processes a/automated sentence parsing oj' Free Texts. That is, it is aimed at developing a prototype and conducting an experimel with it. The result obtained (95% on the training test and 885% on the test set) using the small manually parsed sentences encourage birther research to be launched. especially with the aim of developing fill~fledged Oromo sentence parser.