  For languages with simple morphology, such as English, automatic annotation
pipelines such as spaCy or Stanford's CoreNLP successfully serve projects in
academia and the industry. For many morphologically-rich languages (MRLs),
similar pipelines show sub-optimal performance that limits their applicability
for text analysis in research and the industry.The sub-optimal performance is
mainly due to errors in early morphological disambiguation decisions, which
cannot be recovered later in the pipeline, yielding incoherent annotations on
the whole. In this paper we describe the design and use of the Onlp suite, a
joint morpho-syntactic parsing framework for processing Modern Hebrew texts.
The joint inference over morphology and syntax substantially limits error
propagation, and leads to high accuracy. Onlp provides rich and expressive
output which already serves diverse academic and commercial needs. Its
accompanying online demo further serves educational activities, introducing
Hebrew NLP intricacies to researchers and non-researchers alike.
