Abstract:
Most Sri Lankans use Sinhala text processing in their day to
day activities.
But, they feel it hard to type documents in Sinhala and also it takes more
time and involves typing mistakes and therefore efficiency is low.
Integration of word prediction facility helps the user to select words rather
than typing the word s repeatedly to reduce the number of required
keystrokes, minimize mistakes and reduce time. The aim of this research is
to explore the use of Natural Language Processing and Machine Learning
techniques to assist Sinhala typing tasks by predicting the word s.
We predict the next word to type from n
gram probabilistic model which
involves bi gram, tri gram and a combi nation of bi gram and tr i gram. This
composite n gram model includes both bi gram and tri gram, giving high
priority to the tri gram suggestions . The n gram corpus is generated from
Sinhala corpus collected from online Sinhala newspapers. A maximum
prediction percentage of 41 was achieved for sports documents by using
domain specific n gram corpus of sports documents and obtained an 18.1%
average keystroke reduction by using the prediction model. We tested with
other news categories such as political, legal and local collected from local
newspapers as well. According to our experimental results, composite n
gram model outperformed bi gram and tri g ram word prediction models and
the domain specific composite n gram model performs better than the
composite model created from a mixed corpus.
Our goal is to automatically cluster the document corpus and classify the
edited text after entering certain amo unt of text and get the predictions from
a relevant cluster dynamically to improve the accuracy at runtime, giving a
more relevant prediction.
