Overview of spaCy conventions
Every NLP application consists of several steps of processing the text. As you can see in the first chapter, we have always created instances called nlp and doc. But what did we do exactly?
When we call nlp on our text, spaCy applies some processing steps. The first step is tokenization to produce a Doc object. The Doc object is then processed further with a tagger, a parser, and an entity recognizer. This way of processing the text is called a language processing pipeline. Each pipeline component returns the processed Doc and then passes it to the next component:
Figure 2.1 – A high-level view of the processing pipeline
A spaCy pipeline object is created when we load a language model. We load an English model and initialize a pipeline in the following code segment:
import spacy
nlp = spacy.load("en_core_web_md")
doc = nlp("I went there")
What happened exactly in the preceding code is as follows...