Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, Christopher D. Manning


Abstract
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Stanza on a total of 112 datasets, including the Universal Dependencies treebanks and other multilingual corpora, and show that the same neural architecture generalizes well and achieves competitive performance on all languages tested. Additionally, Stanza includes a native Python interface to the widely used Java Stanford CoreNLP software, which further extends its functionality to cover other tasks such as coreference resolution and relation extraction. Source code, documentation, and pretrained models for 66 languages are available at https://stanfordnlp.github.io/stanza/.
Anthology ID:
2020.acl-demos.14
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2020
Address:
Online
Editors:
Asli Celikyilmaz, Tsung-Hsien Wen
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–108
Language:
URL:
https://aclanthology.org/2020.acl-demos.14
DOI:
10.18653/v1/2020.acl-demos.14
Bibkey:
Cite (ACL):
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 101–108, Online. Association for Computational Linguistics.
Cite (Informal):
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages (Qi et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-demos.14.pdf
Video:
 http://slideslive.com/38928620
Code
 stanfordnlp/stanza +  additional community code
Data
CoNLL 2003