What is best practice for applying traditional NLP extraction techniques a pre-processing for ML models?
Given a pipeline:
- Collect raw data.
- Parse full data set with a variety of traditional NLP techniques, to create model-compatible features (e.g. one-hot encoded matrix of entity extraction).
- Train a ML model on the data.
My intuition says you must split the data inbetween step 1 and 2, for example, only running TF-IDF or NMF on your training set.
But, I have seen a lot in papers and production, that non-deep learning NLP techniques are often used before a data split.