---
title: "Stanza: A Python Natural Language Processing Toolkit for Many Human Languages — Qi 2020"
description: "Stanza is a Python toolkit whose pipeline is neural from raw text to dependencies, for 66 languages. 20 references, cited by 916 papers here."
authors: ["Peng Qi", "Yuhao Zhang", "Yuhui Zhang", "Jason Bolton", "Christopher D. Manning"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations"
source: "https://aclanthology.org/2020.acl-demos.14/"
doi: "10.18653/v1/2020.acl-demos.14"
arxiv: "2003.07082"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qi-2020-stanza-python-natural-language-processing/"
---
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# Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

**Authors:** [Peng Qi](https://md.research.elamur.ai/free/en/authors/peng-qi/), [Yuhao Zhang](https://md.research.elamur.ai/free/en/authors/yuhao-zhang/), Yuhui Zhang, [Jason Bolton](https://md.research.elamur.ai/free/en/authors/jason-bolton/) et al. · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the ACL: System Demonstrations **Link:** [aclanthology.org/2020.acl-demos.14](https://aclanthology.org/2020.acl-demos.14/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**1,431** citations](https://openalex.org/works?filter=cites:W3037109418) OpenAlex, 2026-10-14 · [**916** citations in our library](https://md.research.elamur.ai/free/en/papers/qi-2020-stanza-python-natural-language-processing/cited-by/) of the 140,884 papers whose bibliographies we read · [Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations](https://aclanthology.org/volumes/2020.acl-demos/) · [licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.acl-demos.14/)
- [PDF](https://aclanthology.org/2020.acl-demos.14.pdf)
- [DOI 10.18653/v1/2020.acl-demos.14](https://doi.org/10.18653/v1/2020.acl-demos.14)
- [arXiv 2003.07082](https://arxiv.org/abs/2003.07082)
- [OpenAlex](https://openalex.org/W3037109418)

**Authors (5)**

1. [Peng Qi](https://openalex.org/A5041658523) [ORCID](https://orcid.org/0000-0001-9378-3355) · [Semantic Scholar](https://www.semanticscholar.org/author/50531624) · h 11 · 1,488 citations · 31 works
2. [Yuhao Zhang](https://openalex.org/A5100349352) [ORCID](https://orcid.org/0009-0006-7281-9770) · [Semantic Scholar](https://www.semanticscholar.org/author/49889487) · h 20 · 4,770 citations · 109 works
3. [Yuhui Zhang](https://openalex.org/A5100624916) [ORCID](https://orcid.org/0000-0002-5769-3456) · [Semantic Scholar](https://www.semanticscholar.org/author/49889860) · h 19 · 2,648 citations · 106 works
4. Jason Bolton [Semantic Scholar](https://www.semanticscholar.org/author/40756403)
5. [Christopher D. Manning](https://openalex.org/A5046006076) [ORCID](https://orcid.org/0000-0001-6155-649X) · [Semantic Scholar](https://www.semanticscholar.org/author/144783904) · h 139 · 180,101 citations · 770 works

Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart.

## What it is about

Stanza is a Python toolkit whose pipeline is neural from raw text to dependencies, for 66 languages. It has a combined tokenizer and sentence splitter, multi-word-token expansion (a frequency lexicon with a sequence-to-sequence model), a POS and feature tagger that conditions XPOS and features on UPOS, a lemmatizer (dictionary plus neural), and a Bi-LSTM deep biaffine parser. The parser is extended with two predicted features, the linear order of two words and their typical distance. Models are trained on UD v2.5 and evaluated on 112 datasets; the paper reports the macro average over 100 treebanks and compares with UDPipe and spaCy on five major languages, where Stanza is best on most scores. It is slower than spaCy and is competitive with toolkits of similar accuracy, especially on GPU.

## For Mova

- I1. Order and distance features for `en`: predict, for each candidate arc, whether the head precedes the dependent and the typical distance between them, and add these as features in `en/src/parse.rs`; measure UAS and LAS on EWT test against the current 84.27 and 80.24.
- I2. Lexicon-first multi-word expansion: check how `en/src/tok.rs` handles contractions and clitics (don't, can't, gonna) against EWT test tokens, and count the forms the lexicon misses; the paper uses a lexicon with a model as fallback.
- I3. Macro report: add a macro average over all mova dialect banks to `en ud-eval`, as the paper reports, so that a single gain on EWT cannot hide a loss elsewhere; test sets only where licence allows.

The code is `en/src/tok.rs`, `en/src/parse.rs` and `en/src/ud.rs`. The chapters are [languages/en/07-punctuation-capitals-numbers.md](https://mova.elamur.ai/free/en/docs/languages/en/07-punctuation-capitals-numbers/) (tokenization) and `19-lexicon-mwe-register.md` (multi-word expressions). The paper's lesson for Mova is the reporting: a macro average over many treebanks and a per-stage table, not one headline number.

## Institutions

[🎓**Stanford University** Stanford · United States 248 works · 214,804 citations](https://md.research.elamur.ai/free/en/universities/stanford-university/)

## Cite

Cite as Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton and Christopher D. Manning. 2020. Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations.

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.acl-demos.14](https://doi.org/10.18653/v1/2020.acl-demos.14) · [arXiv 2003.07082](https://arxiv.org/abs/2003.07082)

```bibtex
@inproceedings{qi2020stanza,
  title = {{Stanza: A Python Natural Language Processing Toolkit for Many Human Languages}},
  author = {Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton and Christopher D. Manning},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations},
  year = {2020},
  doi = {10.18653/v1/2020.acl-demos.14},
  eprint = {2003.07082},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.acl-demos.14}
}
```

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- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/)

## References

20 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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