Abstract:
This research proposes an automated system for the identification and
classification of compound nouns in Sinhala, one of the most complex and
crucial features of the language. In Sinhala compound nouns, a prefix is
often combined with meaningful stems to convey specific semantic and
grammatical roles. As part of this, two major types of Sinhala compound
nouns will be addressed in this research, namely Avya Samasa and
Dakarartha Samasa. This study aims to build an effective NLP system for
correctly identifying the types of compound nouns. The system studies
collocations such as "ගස් ද වැල් ද" forming "ගස්වැල්" and "පරසිරිත්"
from "පර"and "සිරිත්". The methodology to be followed is creating a
dataset of 6,000 Sinhala compound nouns by using 43 frequent Sinhala
prefixes, together with POS tagging to ensure that the stems are valid nouns,
verbs, or adjectives. Various machine learning algorithms were applied; these
include Random Forest, Support Vector Machines (SVM), and Naive Bayes.
The highest accuracy attained was by Naive Bayes at 75%. POS tagging
plays a very important role in verifying the grammatical integrity of stems,
and filtering out non-compound words when the stem is not meaningful.
Results show that this system enhances the classification of Sinhala
compound nouns and decreases the complexity of identifying valid
compound forms. However, due to the limited NLP resources in Sinhala, as
well as the morphological complexity of the language itself, some problems
are encountered. The system is of relevance both for linguistic research and
teaching, to learners of Sinhala for a better comprehension of compound
formations. Future studies may be done to expand the dataset and refine
machine learning techniques to increase the accuracy. The limitations of the
paper include dependence on the quality of the dataset, and it is challenging
to handle all minute differences in the linguistic subtleties in handling
different types of compound nouns, where deep learning models may come
in handy.
