NLP Classification: rule-based approach providing high precision but low recall In NLP, rule-based/linguistic approaches are usually said to provide relatively high precision but low recall compared to for example machine learning approaches. I think this is often true for almost any classification task in NLP. However, I couldn't find a paper which specifically examines this phenomena. Can someone please name one? Alternatively, I'd be interested in a paper which generally compares different approaches in NLP classification tasks.

I did quite some googling but all I found is a very vague statement in here: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-285
I dont have enough reputation to add this as comment. Maybe this survey of text classification algorithms (including rule-based approach) could help you http://www.time.mk/trajkovski/thesis/text-class.pdf