Lab03: spaCy NLP pipelinesΒΆ
spaCy makes use of two core types of objects to support various NLP tasks.
A container object in spaCy groups multiple elements into a single unit. It can be a collection of objects, like tokens or sentences, or a set of annotations related to a single object.
Pipeline components objects that process the text input to create containers and fill them with relevant data, such as a part-of-speech tagger, a dependency parser and an entity recogniser.
In this lab, we will look at these two types of objects in spaCy to get a more in-depth understanding of how spaCy NLP code works. The we will test out our information extraction skills by deploying a simple rule-based chatbot.
Reference: (The book code works with spaCy v2.2, our lab code is compiled on spaCy v3.0.5)
