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.
What are the best practices for feature engineering in natural language processing?
Choosing between sparse lexical features and dense semantic featuresTokenization choices and text normalization trade-offsWhen TF-IDF still works well for text classificationHow embeddings complement or replace manual featuresHow preprocessing affects downstream model quality