Use pre-trained NLP-models, ingest into Elastic Search, and enjoy auto-generated Kibana dashboards!
Ever wanted to try out NLP methods but it felt it too cumbersome to set up a workflow for textual data? How to enrich your data based on textual features and explore the results?
NLPeasy (https://github.com/d-one/NLPeasy) does that: Enrich the data using well-known pre-trained models (Word embeddings, Sentiment Analysics, POS, Dependency Parsing). Then start the Elastic Stack on your Docker. Set-up indices and ingest it in bulk. And finally generate Kibana dashboards to explore the results.
Complicated? Not at all! Just do it in a simple Jupyter Notebook.
In this presentation we will give an architecture overview of the different components and demonstrate the capabilities of this Python package.