Difference between sparse features


I’m using: EmbeddingIntentClassifier for Intent Classification.

According to RASA’s documentation, EmbeddingIntentClassifier requires dense_features and/or sparse_features.

In my NLU pipeline, I have CountVectorsFeaturizer and RegexFeaturizer and they both creates sparse_features.

I want to know which one of these two components’ output: CountVectorsFeaturizer and RegexFeaturizer is used as an input for EmbeddingIntentClassifier?

What’s the role of each sparse_features?

Thank you,

CountVectorsFeaturizer is the component used for EmbeddingIntentClassifier.

Explanation here: