  Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated
as classification over a sequence of tokens. Morphologically-Rich Languages
(MRLs) pose a challenge to this basic formulation, as the boundaries of Named
Entities do not necessarily coincide with token boundaries, rather, they
respect morphological boundaries. To address NER in MRLs we then need to answer
two fundamental questions, namely, what are the basic units to be labeled, and
how can these units be detected and classified in realistic settings, i.e.,
where no gold morphology is available. We empirically investigate these
questions on a novel NER benchmark, with parallel tokenlevel and morpheme-level
NER annotations, which we develop for Modern Hebrew, a morphologically
rich-and-ambiguous language. Our results show that explicitly modeling
morphological boundaries leads to improved NER performance, and that a novel
hybrid architecture, in which NER precedes and prunes morphological
decomposition, greatly outperforms the standard pipeline, where morphological
decomposition strictly precedes NER, setting a new performance bar for both
Hebrew NER and Hebrew morphological decomposition tasks.
