Neural Network and Rule-Based Methods for Part-of-Speech Tagging: Hybrid Approach for Amharic POS Tagger

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 POS 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. To evaluate the proposed method, a number of experiments have been conducted. We believe this work will serve as a framework to develop POS tagger for any language with a better efficience.