https://github.com/argilla-io/kcap17-tutorial
Material for tutorial "Hybrid techniques for knowledge-based NLP: Knowledge graphs meet machine learning and all their friends" at KCAP 2017, Austin (Texas)
Science Score: 13.0%
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Low similarity (5.7%) to scientific vocabulary
Repository
Material for tutorial "Hybrid techniques for knowledge-based NLP: Knowledge graphs meet machine learning and all their friends" at KCAP 2017, Austin (Texas)
Basic Info
- Host: GitHub
- Owner: argilla-io
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: http://expertsystemlab.com/kcap2017/
- Size: 2.78 MB
Statistics
- Stars: 16
- Watchers: 3
- Forks: 3
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
KCAP 2017 Tutorial: NLP and Knowledge-based methods with spaCy
Material for tutorial @KCAP 2017 "Hybrid techniques for knowledge-based NLP: Knowledge graphs meet machine learning and all their friends"
Pre-requisites
This tutorial uses Python 3 and Jupyter Notebooks, which you can install via pip or Anaconda (http://jupyter.readthedocs.io/en/latest/install.html)
Install spaCy for your platform using the nice web helper at: https://spacy.io/usage/#section-quickstart
Contents
- Get started with spaCy notebook provides a quick intro to spaCy
- Writing custom components and Entity Linking with spaCy notebook provides an intro to custom components and uses a small library for Entity Linking with spaCy and AGDISTIS (https://github.com/dice-group/AGDISTIS.)
libContains a small library for entity linking with spaCy
Owner
- Name: Argilla
- Login: argilla-io
- Kind: organization
- Email: contact@argilla.io
- Website: https://argilla.io
- Twitter: argilla_io
- Repositories: 12
- Profile: https://github.com/argilla-io
Building the open-source tool for data-centric NLP
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