Amharic Part-of-Speech Tagging Using Hybrid Approach (Neural Network and Rule-Based)
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Date
2008-09-01
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Addis Ababa University
Abstract
The accumulation of information in this electronic age is rapidly increasing. Yet we have very little intelligent tools that will help individuals manage this giant information. Natural Language Processing researches are looking closely at this problem and try to build systems that can understand natural languages. Part-of-speech tagging is one attempt in the effort of understanding human languages. It is the assignment of a category to a word which indicates the role of the
word in a given context.
There are a lot of part-of-speech taggers for many languages but is not for Amharic language. This study proposes a hybrid method of Neural Network and Rule-based approach for tagging Amharic words. So this method is based firstly on Neural Network and then anomaly is corrected by Rule-based approach. Back Propagation algorithm and Transformation- Based learning method are adopted for the development of Amharic tagger.
Building the tagger with hybrid approach can improve the performance of the tagger. This study sets better Amharic tag sets, large size corpus and uses two methods for better accuracy. To evaluate the proposed method, a number of experiments have been conducted. A large number of data are used to train and test the tagger. The experimental result of this thesis work indicates that 91 % and 94% for rule-based and neural network tagger, respectively. But the result reaches to 98% when the experiment has been conducted on the hybrid tagger
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Keywords
Part-aI-Speech, Amharic Part-afSpeech taggeI', Tagsel, POS tagger