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For languages with simple morphology, such as English, automatic annotationpipelines such as spaCy or Stanford's CoreNLP successfully serve projects inacademia and the industry. For many morphologically-rich languages (MRLs),similar pipelines show sub-optimal performance that limits their applicabilityfor text analysis in research and the industry.The sub-optimal performance ismainly due to errors in early morphological disambiguation decisions, whichcannot be recovered later in the pipeline, yielding incoherent annotations onthe whole. In this paper we describe the design and use of the Onlp suite, ajoint morpho-syntactic parsing framework for processing Modern Hebrew texts.The joint inference over morphology and syntax substantially limits errorpropagation, and leads to high accuracy. Onlp provides rich and expressiveoutput which already serves diverse academic and commercial needs. Itsaccompanying online demo further serves educational activities, introducingHebrew NLP intricacies to researchers and non-researchers alike.

What's Wrong with Hebrew NLP? And How to Make it Right