Best Practices for NLP Features

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Problem

Scenario

You are working on a text classification problem and need to decide how to represent raw text for modeling. The quality of your features will affect both model performance and how easy the system is to debug and maintain.

Question

What are the best practices for feature engineering in natural language processing?

What This Tests

Choosing between sparse lexical features and dense semantic features
Tokenization choices and text normalization trade-offs
When TF-IDF still works well for text classification
How embeddings complement or replace manual features
How preprocessing affects downstream model quality