Abstract:
"
Text classification facilitates the ability to classify text data into multiple categories
by assigning labels. It is a core piece of Natural language processing, consisting of a
wide range of use cases, including fake-news detection, sentiment classification, hate speech / cyberbullying detection, user intent classification, news-article classification,
and many more.
Sinhala is being the most used language in Sri Lanka, which is morphologically rich
and agglutinative. Therefore it is complex compared with languages like English,
which has a simple morphology. Furthermore, Sinhala has its own writing system
where the solutions developed for English might not be reusable.
Due to these complexities and being a low resource language, there is no proper
generic and automated solution to perform Sinhala text classification. Even the
current task-specific text classification approaches have not considered the polysemy
of words and were not focused on addressing data scarcity issues.
Therefore, AdaptText has been developed as a novel generic architecture and a
technique for text classification in Sinhala. In order to measure the efficiency of the
proposed novel technique, cross-domain testing and evaluation have been performed
with multiple datasets and againt current best performing approaches. AdaptText
could address the discussed research gaps and could achieve state-of-the-art results
for both binary and multiclass Sinhala text classification."
