CKIP CoreNLP Toolkit
Features
Sentence Segmentation
Word Segmentation
Part-of-Speech Tagging
Named-Entity Recognition
Constituency Parsing
Coreference Resolution
Git
https://github.com/ckiplab/ckipnlp
PyPI
https://pypi.org/project/ckipnlp
Documentation
https://ckipnlp.readthedocs.io/
Online Demo
Contributers
Wei-Yun Ma at CKIP (Maintainer)
Installation
Requirements
Python 3.6+
TreeLib 1.5+
CkipTagger 0.1.1+ [Optional, Recommended]
CkipClassic 1.0+ [Optional]
TensorFlow / TensorFlow-GPU 1.13.1+, <2 [Required by CkipTagger]
Driver Requirements
Driver |
Built-in |
CkipTagger |
CkipClassic |
|---|---|---|---|
Sentence Segmentation |
✔ |
||
Word Segmentation† |
✔ |
✔ |
|
Part-of-Speech Tagging† |
✔ |
✔ |
|
Constituency Parsing |
✔ |
||
Named-Entity Recognition |
✔ |
||
Coreference Resolution‡ |
✔ |
✔ |
✔ |
† These drivers require only one of either backends.
‡ Coreference implementation does not require any backend, but requires results from word segmentation, part-of-speech tagging, constituency parsing, and named-entity recognition.
Installation via Pip
No backend (not recommended): pip install ckipnlp.
With CkipTagger backend (recommended): pip install ckipnlp[tagger] or pip install ckipnlp[tagger-gpu].
With CkipClassic backend: Please refer https://ckip-classic.readthedocs.io/en/latest/main/readme.html#installation for CkipClassic installation guide.
Usage
See https://ckipnlp.readthedocs.io/ for API details.
License
Copyright (c) 2018-2020 CKIP Lab under the CC BY-NC-SA 4.0 License.
1 maintainer
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