Real Time Word Prediction Using N-Grams Model
Jaysidh Dumbali1, Nagaraja Rao A.2
1Jaysidh Dumbali, Vellore Institute of Technology Vellore (Tamil Nadu), India.
2Nagaraja Rao A, Vellore Institute of Technology Vellore (Tamil Nadu), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 870-873 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3050038519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predicting the most probable word for immediate selection is one of the most valuable technique for enhancing the communication experience. With growth in mobile technologies and vast spread of the internet, socializing has become much easier. People around the world spend more and more time on their mobile devices for email, social networking, banking and a variety of other activities. Due to fast paced nature of such conversation it is necessary to save as much as time possible while typing. Hence a predictive text application is necessary for this. Text prediction is one of the most commonly used techniques for increasing the rate of communication. However, the speed at which text is predicted is also very important in this case. The objective of this work is to design and implement a new word predictor algorithm that suggests words that are grammatically more appropriate, with a lower load for system and significantly reduce the amount of keystrokes required by users. The predictor uses a probabilistic language model based on the methodology of the N-Grams for text prediction
Keyword: Natural Language Processing, N-Grams Stupid Backoff, Predictive Text Analytics, GoodTuring algorithm.
Scope of the Article: Regression and Prediction