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|Title: ||AN INTEGRATED APPROACH TO AUTOMATIC COMPLEX SENTENCE PARSING FOR AMHARIC TEXT|
|Authors: ||DANIEL, GOCHEL|
|Advisors: ||W/rt Atelach Alemu|
Ato Mesfin Getachew
Dr. Abebe G/Tsadik
|Keywords: ||INFORMATION SCIENCE|
|Copyright: ||Jun-2003 |
|Date Added: ||16-May-2012 |
|Publisher: ||ADDIS ABABA UNIVERSITY|
|Abstract: ||Natural language processing is a research area which is becoming increasingly popular
each day for both academic and commercial reasons. Higher NLP systems (e.g., machine
translation) are materialized only when the lower ones (e.g., part-of-speech tagger,
syntactic parser) are successfully built. This functional dependency exists even among the
lower NLP systems. A morphological analyzer can be an important component for a partof-
speech (POS) tagger particularly in dealing with unknown words. A POS tagger,
which is a system that uses various sources of information to assign possibly unique
POSs to words, in turn, can be used as an input to a syntactic parser. Writers in the area
of NLP argue that if the POS tagger is accurate, this method is an excellent one. This
thesis can be taken as an attempt to integrate ideas and outputs of previously attempted
Amharic NLP prototypes towards solving a bit further problem in the NLP of the
language, i.e. automatic Amharic complex sentence parsing.
Syntactic parsing underlies most of the applications in natural language processing.
Parsers are already being used extensively in a number of disciplines such as in
computer science (for compiler construction, database interfaces, artificial intelligence,
etc), and in linguistics (for text analysis, corpora analysis, machine translation, etc.).
Although there have been some comprehensive studies of Amharic syntax from a
linguistic perspective, attempts for investigating it from a computational point of view is
a very recent story. In this thesis, Amharic word and phrase classes, sentence formalisms,
morphological properties peculiar to complex sentence formation in the language, and
attempts to extract such features that enable implementation of automatic Amharic
complex sentence parser is presented.
The sample data used in this study has been taken from references that are widely used
in the teaching-learning process of the language. This data has also been manually
analyzed, tagged, parsed, and then used as a corpus to extract the grammar rules and to
assign probabilities. Algorithms that can use the morphological, lexical and syntactic
properties of the language have been customized and modified.
Experiments have been conducted in this study using the training set and test set. The
first experiment was conducted on the part-of-speech tagger to see the state of its
performance when a morphological analysis is embedded in it. The result of this
experiment showed that the tagger attained 98.7% and 94% of accuracies on the training
set and the test set, respectively.
The experiments on complex sentence parsing showed 89.6% accuracy result on the
training set and 81.6% accuracy result on the test set prepared for this purpose.|
|Appears in:||Thesis - Information Science|
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