Open-Source Web Service with Morphological Dictionary-Supplemented Deep Learning for Morphosyntactic Analysis of Czech
This provides an improved tool for Czech language processing, though it is incremental as it builds on existing methods.
The paper tackles morphosyntactic analysis of Czech by combining a deep learning model with a morphological dictionary for rescoring, achieving a 50% error reduction in lemmatization and 58% error reduction in POS tagging over the baseline MorphoDiTa.
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.