NLP Tutorial 7/2 - Introduction to Processing Pipeline in SpaCy Python Tutorial | SpaCy for NLP

spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. If you’re working with a lot of text, you’ll eventually want to know more about it. For example, what’s it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other? spaCy is designed specifically for production use and helps you build applications that process and “understand” large volumes of text. It can be used to build information extraction or natural language understanding systems or to pre-process text for deep learning. Below are some of the spaCy’s features and capabilities. Some of them refer to linguistic concepts, while others are related to more general machine learning functionality.
When you call NLP on a text, spaCy first tokenizes the text to produce a Doc object. The Doc is then processed in several different steps – this is also referred to as the processing pipeline. The pipeline used by the default models consists of a tagger, a parser, and an entity recognizer. Each pipeline component returns the processed Doc, which is then passed on to the next component.

🔊 Watch till last for a detailed description
03:15 Processing text
09:50 How pipelines work?
13:20 Disable and modify pipeline components

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[ijeffking]

I really like your approach to explaining NLP in general. Thank you.

[sweetytripathi]

Why we are disabled any of label in pipe what is the purpose behind this .I think it doesn't matter if we include all these.
Is this related to speed during text processing.