2024-07-07
Background on the Problem
- Communication Efficiency: In today’s fast-paced digital world, efficient communication is critical.
- Typing Assistance: Predictive text models can significantly enhance typing efficiency by suggesting the next word in real-time.
- Business Need: Implementing a robust next-word prediction model can improve user experience in various applications, such as messaging platforms, email clients, and content creation tools.
How the Model Works
- Data Collection: model was trained on a corpus consisting of sample of text data drawn from three files in english from news, blogs, and twitter (now X)
- Preprocessing: The text data was preprocessed to remove noise and perform lemmatization.
- N-gram Model: The core of our model is based on n-grams (bigrams and trigrams) to predict the next word.
- Backoff Strategy: If no trigram match is found, the model falls back to bigrams, co-occurrence data, and finally unigrams.
- User Experience: Simply type a sentence and click a button (or press enter) and the app predicts the next word.
Co-occurrence Data Integration
- Why Co-occurrence Data?
- Helps to improve predictions when specific n-gram matches are not found.
- Captures word pairs that frequently appear together, providing better context.
- Impact on Model Performance:
- Incorporating co-occurrence data enhances the model’s ability to predict relevant next words, even with limited n-gram matches.
- Future Scope:
- Further refine co-occurrence data integration for even better performance.
Potential Improvements: with more compute power and server storage, we could use
- More Data:
- Use a larger corpus of text data to train the model, improving its accuracy and robustness.
- Higher-Level N-grams:
- Incorporate higher-level n-grams (e.g., fourgrams, fivegrams) to capture more context and provide better predictions.
- Enhanced Context Understanding:
- Expand the use of co-occurrence data to improve context understanding and suggest more relevant next words.