StanfordNLP release.
New Python #NLProc package. StanfordNLP provides native, neural (PyTorch) tokenization, POS tagging and dependency parsing for 53 languages based on UD v2 — and a Python CoreNLP interface.
Pip:
PyPI: https://pypi.org/project/stanfordnlp/
Link: https://stanfordnlp.github.io/stanfordnlp/
#NLP #Stanford
New Python #NLProc package. StanfordNLP provides native, neural (PyTorch) tokenization, POS tagging and dependency parsing for 53 languages based on UD v2 — and a Python CoreNLP interface.
Pip:
pip install stanfordnlpPyPI: https://pypi.org/project/stanfordnlp/
Link: https://stanfordnlp.github.io/stanfordnlp/
#NLP #Stanford
PyPI
stanfordnlp
Official Stanford NLP Python Library
Stanford updated tool Stanza with #NER for biomedical and clinical terms
Stanza extended with first domain-specific models for biomedical and clinical medical English. They range from approaching to significantly improving state of the art results on syntactic and NER tasks.
That means that now neural networks are capable of understanding difficult texts with lots of specific terms. That means better search, improved knowledge extraction and approach for performing META analysis, or even research with medical ArXiV publications.
Demo: http://stanza.run/bio
ArXiV: https://arxiv.org/abs/2007.14640
#NLProc #NLU #Stanford #biolearning #medicallearning
Stanza extended with first domain-specific models for biomedical and clinical medical English. They range from approaching to significantly improving state of the art results on syntactic and NER tasks.
That means that now neural networks are capable of understanding difficult texts with lots of specific terms. That means better search, improved knowledge extraction and approach for performing META analysis, or even research with medical ArXiV publications.
Demo: http://stanza.run/bio
ArXiV: https://arxiv.org/abs/2007.14640
#NLProc #NLU #Stanford #biolearning #medicallearning
