docker-compose up
- Python >= 3.9
python app.py
python -m gunicorn -w 1 app:app
- Node.js >= 10
First, run the development server:
cd frontend/
npm ci
npm run dev
Open http://localhost:3000 with your browser to see the result.
npm ci
npm start
Application that predicts business events, extract named entities and topics from news articles
Home Page: https://jde-predict.tools.eurecom.fr/
docker-compose up
python app.py
python -m gunicorn -w 1 app:app
First, run the development server:
cd frontend/
npm ci
npm run dev
Open http://localhost:3000 with your browser to see the result.
npm ci
npm start
In the demo UI (https://jde-predict.tools.eurecom.fr/), replace:
In the ISWC 2023 demo paper, we have evaluated the results of the predictions on articles taken from the same source (JDE) than the one being used for training the supervised model for predicting business events.
Let's select 5 articles from Les Echos, Le Figaro and Le Monde, which are related to financial/business and run our predictions: business events (4 algorithms), themes and named entities. Let's evaluate a posteriori the results. A markdown file detailing the articles chosen, results obtained and analysis should be produced
The demonstration at https://jde-predict.tools.eurecom.fr/ enables to submit any article from the JDE and to visualize a number of annotations on this article namely:
The goal is to materialize these annotations in a KG. The tasks are:
skos:Concept
in a dedicated ConceptScheme. Similarly, the Themes could also be represented as skos:Concept
in another ConceptScheme. Named Entity annotations could re-use the NIF ontology.A declarative, efficient, and flexible JavaScript library for building user interfaces.
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