Entity extraction

In this section, we'll implement the first step of our chatbot NLU pipeline and extract entities from the dataset utterances. The following are the entities marked in our dataset:

city
date
time
phone_number
cuisine
restaurant_name
street_address

To extract the entities, we'll use the spaCy NER model and the spaCy Matcher class. Let's get started by extracting the city entities.

Extracting city entities

We'll first extract the city entities. We'll get started by recalling some information about the spaCy NER model and entity labels from Chapter 3, Linguistic Features, and Chapter 6, Putting Everything Together: Semantic Parsing with spaCy:  

• First, we recall that the spaCy named entity label for cities and countries is GPE. Let's ask spaCy to explain what GPE label corresponds to once again:

  import spacy
  nlp = spacy.load("en_core_web_md")
  spacy.explain("GPE")
  'Countries, cities, states...