Natural Language Processing in Practice
Natural Language Processing in Practice Natural language processing (NLP) helps machines understand human text and speech. In practice, start with a real problem you can measure and keep the scope small. A focused task produces faster learning and clearer results. A practical workflow Define the goal and how you will measure success. Collect a small, representative labeled dataset and split it for training and testing. Preprocess text: normalize case, handle noise, and tokenize. Choose features that fit the task. Choose a modeling path. Start with a simple baseline like a rule or a lightweight classifier. If needed, use embeddings or a pre-trained model for better performance. Evaluate honestly and analyze errors. Track per-class performance and common mistakes. A concrete example Imagine a sentiment classifier for product reviews. Gather 1,000–2,000 labeled reviews, label positive or negative, and split 80/20. Start with a basic bag-of-words model; if accuracy sits near 70–75%, add bigrams or switch to a small neural layer. Keep the model lightweight to ensure fast responses. ...