There are no principle problems. The network doesn’t “know” what a proper sentence is. It only sees a sequence of words (well, word vectors), no matter if it’s a RNN, CNN, or anything more fancy. For example, we trained are NER model for extracting named entities from keyword-based search queries 26 – please note that this was only a proof-of-concept based on synthetic data in the context of this paper.
It’s never obvious what the network will learn. For example, anything followed by “a” or “an” is probably not a named entity. Or maybe the network just learns named entities such as “Italy” by heart.
It also depends on the context. For example, I’ve noticed that SpaCy NER is often very sensitive to capitalization. So I can only use it for formal text but not for, e.g., social media posts. In another work, we looked into NER with focus on points of interests (e.g., names of shops, restaurants, hotels, bars, parks, etc.). The problem is that many names are not as “obivous” as, say, Italy. Consider, the tweet “freshly baked makes the best damn cookies”. We can generally infer that “freshly baked” is some kind of shop or restaurant, but that’s much harder to learn for a machine.
I would simply give it a shot: train an NER model and make a standard error analysis, i.e., try to identify to most common cases where the classification fails, and see if or how you can address those cases.