# CLASSLA-Stanza: The Next Step for Linguistic Processing of South Slavic Languages

arXiv: https://arxiv.org/abs/2308.04255 · HTML: https://mlsanity.com/paper/2308.04255/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)

## Built on by (most cited first, 7 of 7)

- [The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings](https://mlsanity.com/paper/2309.09783/) (2023)
- [The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings](https://mlsanity.com/paper/2409.15397/) (2024)
- [CLASSLA-web: Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation](https://mlsanity.com/paper/2403.12721/) (2024)
- [Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance](https://mlsanity.com/paper/2402.16596/) (2024)
- [CLASSLA-Express: a Train of CLARIN.SI Workshops on Language Resources and Tools with Easily Expanding Route](https://mlsanity.com/paper/2412.01386/) (2024)
- [ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbian](https://mlsanity.com/paper/2511.01619/) (2025)
- [DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling](https://mlsanity.com/paper/2602.11968/) (2026)

## Abstract (arXiv)

We present CLASSLA-Stanza, a pipeline for automatic linguistic annotation of the South Slavic languages, which is based on the Stanza natural language processing pipeline. We describe the main improvements in CLASSLA-Stanza with respect to Stanza, and give a detailed description of the model training process for the latest 2.1 release of the pipeline. We also report performance scores produced by the pipeline for different languages and varieties. CLASSLA-Stanza exhibits consistently high performance across all the supported languages and outperforms or expands its parent pipeline Stanza at all the supported tasks. We also present the pipeline's new functionality enabling efficient processing of web data and the reasons that led to its implementation.