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This paper presents a set of industrial-grade text processing models for
Hungarian that achieve near state-of-the-art performance while balancing
resource efficiency and accuracy. Models have been implemented in the spaCy
framework, extending the HuSpaCy toolkit with several improvements to its
architecture. Compared to existing NLP tools for Hungarian, all of our
pipelines feature all basic text processing steps including tokenization,
sentence-boundary detection, part-of-speech tagging, morphological feature
tagging, lemmatization, dependency parsing and named entity recognition with
high accuracy and throughput. We thoroughly evaluated the proposed
enhancements, compared the pipelines with state-of-the-art tools and
demonstrated the competitive performance of the new models in all text
preprocessing steps. All experiments are reproducible and the pipelines are
freely available under a permissive license.
No creative common's license