https://github.com/cloudera/cml_amp_spacy_entity_extraction

A Jupyter notebook demonstrating entity extraction on headlines with SpaCy.
https://github.com/cloudera/cml_amp_spacy_entity_extraction

entity-extraction named-entity-recognition nlp spacy

Last synced: about 2 months ago
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A Jupyter notebook demonstrating entity extraction on headlines with SpaCy.

• Host: GitHub
• URL: https://github.com/cloudera/cml_amp_spacy_entity_extraction
• Owner: cloudera
• License: apache-2.0
• Created: 2021-01-28T22:16:25.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-04-06T21:32:56.000Z (over 2 years ago)
• Last Synced: 2023-10-20T18:57:46.759Z (about 1 year ago)
• Topics: entity-extraction, named-entity-recognition, nlp, spacy
• Language: Jupyter Notebook
• Homepage:
• Size: 3.36 MB
• Stars: 3
• Watchers: 8
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# Analyzing News Headlines with SpaCy

[SpaCy](https://spacy.io/) wraps industrial-strength natural language processing capabilites into a Python library with an elegant and powerful API. The notebook in this repo demonstrates its use for Named Entity Recognition (NER) on a real world news dataset.

![Sentences with named entities highlighted.](docs/images/NER.png)

We take a public domain dataset of [Reuters news headlines](https://www.kaggle.com/notlucasp/financial-news-headlines) and use spaCy to extract named entities. We demonstrate three example downstream use cases:

- investigating the organizations that appeared most often in Reuters in 2020

- viewing the mentions of any given organization over time

- inspecting which organizations appear in headlines together

## Deploying on Cloudera Machine Learning (CML)

There are three ways to launch this notebook on CML:

1. **From Prototype Catalog** - Navigate to the Prototype Catalog in a CML workspace, select the "Analyzing News Headlines with SpaCy" tile, click "Launch as Project", click "Configure Project"

2. **As ML Prototype** - In a CML workspace, click "New Project", add a Project Name, select "ML Prototype" as the Initial Setup option, copy in the [repo URL](https://github.com/cloudera/CML_AMP_SpaCy_Entity_Extraction), click "Create Project", click "Configure Project"

3. **Manual Setup** - In a CML workspace, click "New Project", add a Project Name, select "Git" as the Initial Setup option, copy in the [repo URL](https://github.com/cloudera/CML_AMP_SpaCy_Entity_Extraction), click "Create Project".

Once the project has been initialized in a CML workspace, run the notebook by starting a Python 3 Jupyter notebook server session. All library and model dependencies are installed inline in the notebook.

Happy hacking!