You are working on an NLP problem where raw text needs to be converted into model-ready representations. You may be solving a classification task, comparing traditional pipelines with transformer-based approaches, or deciding how much preprocessing to apply before training.
How do you approach the problem of feature extraction in natural language processing?
Tokenization choices for different model familiesSparse lexical features such as TF-IDFDense and contextual word embeddingsHow feature extraction changes for text classification