PDTB Style Sentence Level Shallow Discourse Parser for Amharic
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Date
10/10/2020
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Addis Ababa University
Abstract
Research 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,
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Keywords
NLP, Discourse, Discourse Parser, Discourse Marker, Elementary Discourse Unit, Sense, Argument, Machine Learning