Two Kinds of Recall

Understand

It is an established assumption that pattern-based models are good at precision, while learning based models are better at recall.

• But is that really the case? I argue that there are two kinds of recall: d-recall, reflecting diversity, and e-recall, reflecting exhaustiveness.
• I demonstrate through experiments that while neural methods are indeed significantly better at d-recall, it is sometimes the case that pattern-based methods are still substantially better at e-recall.
• Ideal methods should aim for both kinds, and this ideal should in turn be reflected in our evaluations.