Updating an existing pipeline component

In this section, we will train spaCy's NER component further with our own examples to recognize the navigation domain. We already saw some examples of navigation domain utterances and how spaCy's NER model labeled entities of some example utterances:

navigate/0 to/0 my/0 home/0
navigate/0 to/0 Oxford/FAC Street/FAC

Obviously, we want NER to perform better and recognize location entities, such as street names, district names, and other location names, such as home, work, and office. Now, we'll feed our examples to the NER component and will do more training. We will train NER in three steps:

1. First, we'll disable all the other statistical pipeline components, including the POS tagger and the dependency parser.
2. We'll feed our domain examples to the training procedure.
3. We'll evaluate the new NER model.

Also, we will learn how to do the following:

• Save the updated NER model to disk...