Transition Based Dependency Parser for Amharic Language Using Deep Learning

Mizanu Zelalem, Million Meshesha (PhD)

Keywords: Amharic dependency parsing, arc-eager transition, LSTM, Transition action prediction, Relationship type prediction

Abstract: Researches shows that attempts done to apply existing dependency parser on morphological rich languages including Amharic shows a poor performance. In this study, a dependency parser for Amharic language is implemented using arc-eager transition system and LSTM network. The study introduced another way of building labeled dependency structure by using a separate network model to predict dependency relation. This helps the number of classes to decrease from 2n+2 into n, where n is the number of relationship types in the language and increases the number of examples for each class in the data set. Evaluation of the parser model results 91.54 and 81.4 unlabeled and labeled attachment score respectively. The major challenge in this study was the decrease of the accuracy of labeled attachment score. This is mainly due to the size and quality of the tree-bank available for Amharic language. Improving the tree-bank by increasing the size and by adding morphological information can make the performance of parser better.