Automated Word Prediction in Bangla Language Using Stochastic Language Models
This work addresses typing efficiency and accessibility for Bangla language users, particularly benefiting disabled individuals, but it is incremental as it applies existing methods to a new language domain.
The authors tackled word prediction in Bangla language using N-gram models like unigram, bigram, trigram, deleted interpolation, and backoff to save time, reduce keystrokes, and minimize misspelling, achieving promising results.
Word completion and word prediction are two important phenomena in typing that benefit users who type using keyboard or other similar devices. They can have profound impact on the typing of disable people. Our work is based on word prediction on Bangla sentence by using stochastic, i.e. N-gram language model such as unigram, bigram, trigram, deleted Interpolation and backoff models for auto completing a sentence by predicting a correct word in a sentence which saves time and keystrokes of typing and also reduces misspelling. We use large data corpus of Bangla language of different word types to predict correct word with the accuracy as much as possible. We have found promising results. We hope that our work will impact on the baseline for automated Bangla typing.