This blog post is written in the context of creating a text classification model. The objective is to build and train a text classification model to identify topics in a text. These topics are Kubernetes and Watson NLP. As the train and test data, we will use an export from my blog on wordpress.com. This data we will label this with the open-source tool called Label Sleuth.
This blog post contains an extract of a Jupyter Notebook related to how to convert XML data to comma separated value in a Jupyter Notebook.
This blog post is about, how to create a custom dictionary model for Watson NLP. One capability of the Watson NLP is the "Entity extraction to find mentions of entities (like person, organization, or date)." We will adapt the Watson NLP model to extract entities from a given text to find single entities like names and locations which are identified by an entry and its label.
Thomas Suedbroecker's Blog 