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.
- 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.
- Clone the repository.
- Install the required packages.
- Download the necessary NLTK data.
- Load the Reuters dataset.
- Tokenize the text data.
- Convert the tokens into bigrams.
- Use Conditional Frequency Distribution to predict the next word based on the given context.
- 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.
README.md: Project description and setup instructions.- Main script: Script for loading data, processing text, and predicting the next word.
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.