Prediction of Text

Text Prediction

Build a predictive text application using a statistical language model.

• Key Features:

  • Predicts the next word based on user input.

  • Utilizes bigrams and trigrams for accurate predictions.

  • Trained on large text corpora including blogs, news, and tweets.

Solution Overview

• Data Collection:

  • Sampled lines from “en_US.blogs.txt”, “en_US.news.txt”, and “en_US.twitter.txt”.

• N-gram Model:

  • Bigrams: Predict the next word based on the previous word.

  • Trigrams: Predict the next word based on the previous two words.

• Prediction Mechanism:

  • If input has 1 word, use bigrams.

  • If input has 2+ words, use trigrams (fallback to bigrams if no match).

Key Features

• User Interface:

  • Simple text input field for entering phrases.

  • Displays predicted next word(s) based on input.

• Backend:

  • Loads pre-trained bigrams and trigrams from .rds files.

  • Efficiently processes user input to provide real-time predictions. ## Key Featuresll

• User Interface:

  • Simple text input field for entering phrases.

  • Displays predicted next word(s) based on input.

• Backend:

  • Loads pre-trained bigrams and trigrams from .rds files.

  • Efficiently processes user input to provide real-time predictions.

Benefits

• Improves User Experience:

  • Provides accurate and contextually relevant word predictions.

• Applications:

  • Can be used in text completion tools, chatbots, and writing aids.

• Future Enhancements:

  • Extend model to handle more complex n-gram combinations.

  • Incorporate user feedback for continuous improvement.