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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

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  • $\begingroup$ Rather than looking for a single paper that says "rule-based approaches perform poorly in all sorts of NLP tasks", I think it's more reasonable to expect to find papers that each analyze a single specific NLP task and assess rule-based approaches to that task. Thus, you might need to spend some time reading the NLP literature.... $\endgroup$ – D.W. Jan 24 '16 at 13:08
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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

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