We use a few cookies

Essential cookies keep the library working. With your permission, we'd also like to use analytics to see what's useful and improve the experience. Read our Privacy Policy or Cookie Policy.

Skip to main content
Home/e-Resources/Computer Science & Information Technology/Automated Word Prediction in Bangla Language Using Stochastic Language Models
PeriodicalsComputer Science & Information Technology

Automated Word Prediction in Bangla Language Using Stochastic Language Models

Checking your access to this record.

Abstract & Overview

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.

Cite this record

Md. Masudul Haque, Md. Tarek Habib, Md. Mokhlesur Rahman (2015). Automated Word Prediction in Bangla Language Using Stochastic Language Models. International Journal in Foundations of Computer Science & Technology, 5(6), 67–75. https://doi.org/10.5121/ijfcst.2015.5607

Copy in another style

Haque, M. M., Habib, M. T., & Rahman, M. M. (2015). Automated Word Prediction in Bangla Language Using Stochastic Language Models. International Journal in Foundations of Computer Science & Technology, 5(6), 67–75. https://doi.org/10.5121/ijfcst.2015.5607
Author names are used as deposited, so check their order before you submit.

Metadata is harvested from open-access indexes and can be incomplete. Check a citation against the published version before relying on it — see our open access & attribution policy.