  We present an open-source web service for Czech morphosyntactic analysis. The
system combines a deep learning model with rescoring by a high-precision
morphological dictionary at inference time. We show that our hybrid method
surpasses two competitive baselines: While the deep learning model ensures
generalization for out-of-vocabulary words and better disambiguation, an
improvement over an existing morphological analyser MorphoDiTa, at the same
time, the deep learning model benefits from inference-time guidance of a
manually curated morphological dictionary. We achieve 50% error reduction in
lemmatization and 58% error reduction in POS tagging over MorphoDiTa, while
also offering dependency parsing. The model is trained on one of the currently
largest Czech morphosyntactic corpora, the PDT-C 1.0, with the trained models
available at https://hdl.handle.net/11234/1-5293. We provide the tool as a web
service deployed at https://lindat.mff.cuni.cz/services/udpipe/. The source
code is available at GitHub (https://github.com/ufal/udpipe/tree/udpipe-2),
along with a Python client for a simple use. The documentation for the models
can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model.
