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Next Word Prediction Project

Overview

The Next Word Prediction project uses the NLTK library and the Reuters dataset to predict the next word in a sequence of text. By converting text data into bigrams and using Conditional Frequency Distribution, this project demonstrates the application of natural language processing (NLP) techniques in predictive text systems.

Features

  • Utilizes the Reuters dataset from the NLTK corpus.
  • Processes text data into bigrams for word prediction.
  • Implements Conditional Frequency Distribution for predicting the next word.
  • Showcases the practical application of NLP techniques.

Installation

  1. Clone the repository.
  2. Install the required packages.
  3. Download the necessary NLTK data.

Usage

  1. Load the Reuters dataset.
  2. Tokenize the text data.
  3. Convert the tokens into bigrams.
  4. Use Conditional Frequency Distribution to predict the next word based on the given context.

Applications

  • Autocomplete Systems: Predictive text systems in search engines and messaging apps use similar techniques to suggest the next word or phrase, improving user experience by speeding up text entry.
  • Language Models: Advanced language models, such as those used in virtual assistants (e.g., Siri, Alexa), use bigrams and other NLP techniques to understand and predict user queries.
  • Text Editors: Writing assistants and text editors (e.g., Grammarly) use next word prediction to provide suggestions and corrections, enhancing writing efficiency and accuracy.

Project Structure

  • README.md: Project description and setup instructions.
  • Main script: Script for loading data, processing text, and predicting the next word.

Conclusion

The Next Word Prediction project illustrates the use of the NLTK library and the Reuters dataset to build a predictive text system. By converting the corpus into bigrams and utilizing Conditional Frequency Distribution, this project provides a practical example of next word prediction in natural language processing.