Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton low, Christopher D. Manning
We introduce Sta n z a , an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Sta n z a features a language-agnostic fully neural pipeline for text analysis, including tokenization, multiword token expansion, lemmatization, part-ofspeech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Sta n z a 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, Sta n z a 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/.
Citation edge evidence
- Transformers: State-of-the-Art Natural Language Processing→Stanza: A Python Natural Language Processing Toolkit for Many Human LanguagesLegacy edge — source detail unavailable
- Stanza: A Python Natural Language Processing Toolkit for Many Human Languages→Enriching Word Vectors with Subword InformationLegacy edge — source detail unavailable
- Stanza: A Python Natural Language Processing Toolkit for Many Human Languages→The Stanford CoreNLP Natural Language Processing ToolkitLegacy edge — source detail unavailable
- Stanza: A Python Natural Language Processing Toolkit for Many Human Languages→Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity RecognitionLegacy edge — source detail unavailable
- Transformers: State-of-the-Art Natural Language Processing→The Stanford CoreNLP Natural Language Processing ToolkitLegacy edge — source detail unavailable
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What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Transformers: State-of-the-Art Natural Language Processing | 2020 | 8,295 |
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Topics
| Topic Modeling | Computer Science |
| Natural Language Processing Techniques | Computer Science |
| Computational Physics and Python Applications | Computer Science |
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