Perplexity, Smoothing, and What Words Mean

1. 1Calculate perplexity using a held-out test set to evaluate a language model's performance
2. 2Apply smoothing techniques to handle unseen words in a language model
3. 3Compare the perplexity of different language models to determine which one is more accurate
4. 4Use perplexity to tune hyperparameters and improve a language model's performance
5. 5Evaluate the effect of smoothing on a language model's perplexity and overall performance

NLP engineers and data scientists can benefit from this knowledge to improve their language models and evaluate their performance

💡 Perplexity is a measure of how well a language model predicts a test set, and smoothing helps handle unseen words

💡 Evaluate language models using perplexity and improve performance with smoothing techniques