# HuSpaCy: an industrial-strength Hungarian natural language processing toolkit

arXiv: https://arxiv.org/abs/2201.01956 · HTML: https://mlsanity.com/paper/2201.01956/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)

## Built on by (most cited first, 8 of 8)

- [The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models](https://mlsanity.com/paper/2404.03189/) (2024)
- [Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines](https://mlsanity.com/paper/2308.12635/) (2023)
- [calamanCy: A Tagalog Natural Language Processing Toolkit](https://mlsanity.com/paper/2311.07171/) (2023)
- [TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping](https://mlsanity.com/paper/2311.02355/) (2023)
- [HunSum-1: an Abstractive Summarization Dataset for Hungarian](https://mlsanity.com/paper/2302.00455/) (2023)
- [Hybrid lemmatization in HuSpaCy](https://mlsanity.com/paper/2306.07636/) (2023)
- [Data Augmentation for Machine Translation via Dependency Subtree Swapping](https://mlsanity.com/paper/2307.07025/) (2023)
- [When Discourse Pressures Conflict: Information Structure in Vision-Language Model Outputs](https://mlsanity.com/paper/2605.28346/) (2026)

## Abstract (arXiv)

Although there are a couple of open-source language processing pipelines available for Hungarian, none of them satisfies the requirements of today's NLP applications. A language processing pipeline should consist of close to state-of-the-art lemmatization, morphosyntactic analysis, entity recognition and word embeddings. Industrial text processing applications have to satisfy non-functional software quality requirements, what is more, frameworks supporting multiple languages are more and more favored. This paper introduces HuSpaCy, an industry-ready Hungarian language processing toolkit. The presented tool provides components for the most important basic linguistic analysis tasks. It is open-source and is available under a permissive license. Our system is built upon spaCy's NLP components resulting in an easily usable, fast yet accurate application. Experiments confirm that HuSpaCy has high accuracy while maintaining resource-efficient prediction capabilities.