Next Word Prediction using N-grams Language Model and Shiny App

• The task of predicting the next word in a sentence or phrase is a common problem in natural language processing.
• It is important to accurately predict the next word in order to improve the quality of natural language processing applications like language translation, text summarization, and text generation.
• However, predicting the next word in a sentence is a difficult task because it depends on the context of the sentence and the vocabulary of the language.

• The data set used to train the model is a collection of text data, which can be downloaded from HC Corpora database.
• The data is preprocessed by tokenizing the words and converting them to numerical values.
• The data is then split into n-grams (in this case, trigrams)
• The algorithm is based on the n-gram model which is a probability based model.
• The n-gram model uses the frequency of words in a text to predict the next word in a sentence.

The “Interactive Web App: Next Word Prediction” is hosted HERE.

App's functionality:

• The Shiny app takes as input a phrase (multiple words) in a text box input and outputs a prediction of the next word.
• The app is built with R and Shiny, an open-source framework for creating web applications in R.
• The app allows users to easily interact with the prediction algorithm and get immediate results.

Benefits of the app:

• The app makes it easy for users to understand and use the algorithm without the need for technical expertise.
• The app allows for real-time interaction with the algorithm, which can be useful for understanding the algorithm's behavior or for testing different inputs.
• The app can be easily shared with others, making it easy to collaborate on the algorithm.