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STANZA

Multilingual NLP library from Stanford

Apache-2.0

ABOUT

Processing natural language across multiple languages requires accurate tokenization, part-of-speech tagging, lemmatization, dependency parsing, and named entity recognition — tasks typically handled by separate, language-specific tools. Stanza solves this by providing a unified Python library that covers the full NLP pipeline for over 70 languages using neural models trained on Universal Dependencies data. Built on PyTorch and developed by the Stanford NLP group, it combines state-of-the-art accuracy with a clean API that lets developers go from raw text to fully annotated linguistic structures in a few lines of code. For multilingual applications, research, and production systems alike, Stanza eliminates the complexity of managing separate NLP pipelines for each language.

INSTALL
pip install stanza

INTEGRATION GUIDE

1. Tokenize, tag, and parse text in over 70 languages with a single unified pipeline 2. Extract named entities from multilingual documents for information retrieval and knowledge base construction 3. Build multilingual chatbots and conversational AI systems that understand sentence structure across languages 4. Prepare linguistic features for downstream NLP tasks like relation extraction and text classification 5. Process biomedical and clinical text with specialized models for the biomedical domain

TAGS

nlpnatural-language-processingpythonpytorchtokenizationnamed-entity-recognitionmultilingualdeep-learningstanford
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