Introduction

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

https://ckip.iis.sinica.edu.tw/service/corenlp

Contributers

• Mu Yang at CKIP (Author & Maintainer)

• Wei-Yun Ma at CKIP (Maintainer)

• DouglasWu

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.