  Named Entity Recognition (NER) is a key NLP task, which is all the more
challenging on Web and user-generated content with their diverse and
continuously changing language. This paper aims to quantify how this diversity
impacts state-of-the-art NER methods, by measuring named entity (NE) and
context variability, feature sparsity, and their effects on precision and
recall. In particular, our findings indicate that NER approaches struggle to
generalise in diverse genres with limited training data. Unseen NEs, in
particular, play an important role, which have a higher incidence in diverse
genres such as social media than in more regular genres such as newswire.
Coupled with a higher incidence of unseen features more generally and the lack
of large training corpora, this leads to significantly lower F1 scores for
diverse genres as compared to more regular ones. We also find that leading
systems rely heavily on surface forms found in training data, having problems
generalising beyond these, and offer explanations for this observation.
