Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines

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 HuSpaCy toolkit with several improvements to its architecture. Compared existing NLP tools Hungarian, all our pipelines feature basic steps including tokenization, sentence-boundary detection, part-of-speech tagging, morphological lemmatization, dependency parsing named entity recognition high accuracy throughput. We thoroughly evaluated proposed enhancements, compared demonstrated competitive new preprocessing steps. All experiments are reproducible freely available under permissive license.