Libsie, Mulugets (PhD)Arega, Robel2021-01-212023-11-292021-01-212023-11-292020-10-10http://etd.aau.edu.et/handle/123456789/24745Research on natural language processing applications (NLP) is a very important topic in our daily life, by enabling computers to understand human languages. Such researches has come a long way in foreign languages like English, Japanese, Chinese, Portuguese and Arabic. NLP applications such as include machine translation, question answering, knowledge extraction and information retrieval are some of the fruits of such researches. Discourse parser is one of the main components that enables the realization of such NLP applications. For foreign languages like English and Arabic, many discourse parsers are developed in different approaches. However, in the case of Amharic, there are no works done, to the best of the researcher’s knowledge, on Amharic discourse parser so far. In this study, a Penn Discourse Tree Bank (PDTB) style sentence level shallow discourse parser for Amharic is developed. We have used machine-learning algorithms to accomplish the subtasks of discourse parsing. The algorithms utilize lexical and positional features of the discourse marker and related words for segmentation and identify associated discourse relation. The parser is tested on test sentences, which are extracted from different sources. Encouraging results are observed from the experiments performed,enNLPDiscourseDiscourse ParserDiscourse MarkerElementary Discourse UnitSenseArgumentMachine LearningThesis