NLP Tutorial 4 - SpaCy Python Tutorial | Linguistic Features Extraction in NLP

This tutorial is a crisp and effective introduction to spaCy and the various NLP linguistic features it offers. We will perform several NLP related tasks, such as Tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and Visualization using displaCy.
spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python.spaCy is designed specifically for production use and helps you build applications that process and understand large volumes of text. It’s written in Cython and is designed to build information extraction or natural language understanding systems to pre-process text for deep learning.
Processing raw text intelligently is difficult: most words are rare, and it’s common for words that look completely different to mean almost the same thing. That’s exactly what spaCy is designed to do: you put in raw text and get back a Doc object, that comes with a variety of Linguistic annotations. spaCy acts as a one-stop-shop for various tasks used in NLP projects, such as Tokenization, Lemmatisation, Part-of-speech(POS) tagging, Name entity recognition, Dependency parsing, Sentence Segmentation, Word-to-vector transformations, and other cleaning and normalization text methods.

? Watch till last for a detailed description
00:39 Understanding problem statement
05:58 Installing the packages
10:17 Tokenization
16:18 POS tagging
27:00 Dependency parsing
31:14 Named entity recognition
34:09 Sentence segmentation
45:36 Visualization

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