NLP Tutorial 5 - Rule Based Text Phrase Extraction and Matching using SpaCy in NLP

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In this video, we will learn about Rule-Based Text Phrase Extraction and Matching using SpaCy in 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.
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
02:57 Installing libraries
04:31 Rule-based matching
11:12 Get started with code
24:47 regular expression
34:28 Wildcard text
39:12 Exclusion
52:00 Finding word

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