Natural Language Text Processing with Python

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Natural Language Text Processing with Python

Video description

Even though computers can't read, they're very effective at extracting information from natural language text. They can determine the main themes in the text, figure out if the writers of the text have positive or negative feelings about what they've written, decide if two documents are similar, add labels to documents, and more.

This course shows you how to accomplish some common NLP (natural language processing) tasks using Python, an easy to understand, general programming language, in conjunction with the Python NLP libraries, NLTK, spaCy, gensim, and scikit-learn. The course is designed for basic level programmers with or without Python experience.

Gain practical hands-on natural language processing experience using Python
Understand how to tokenize text so it can be processed as symbols
Learn to convert text and words to vectors using TF-IDF and word2vec
Explore dependency parsing, sentiment analysis, and LDA topic modeling
Learn to find named entities in text and map them to an external knowledge base
Understand the capabilities and limitations of natural language text processing

Jonathan Mugan is CEO and co-founder of DeepGrammar, a natural language processing company. Jonathan has a PhD in computer science from the University of Texas, and has been working in AI and machine learning since 2003. He describes his research focus as "making the squishy reality of our everyday world available to computation."

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Introduction

Course Introduction 00:02:25

About The Author 00:00:36

Getting Started: Basic String Processing In Python

String Operations 00:04:49

Working With Unicode 00:05:16

Converting Text To Symbols: Tokenization In NLTK and spaCy

Splitting Documents 00:04:41

Splitting Sentences 00:03:20

Filtering Stop Words 00:02:07

Going Subsymbolic: Vector Representations

tf-idf Gensim 00:09:24

Word Vectors 00:03:35

Google Word Vectors 00:04:03

Learn Word Vectors 00:08:07

Finding The Structure Of Text: Parsing In spaCy

Dependency Parsing 00:03:39

Sentence Head 00:02:23

Named Entities 00:03:21

Determining How The Writer Feels: Sentiment Analysis In VADER

Sentiment Analysis Intro 00:03:18

Sentiment In VADER 00:05:13

Making Decisions: Text Classification

Text Classification Intro 00:02:45

Classification With TextBlob 00:10:25

Classification With scikit-learn 00:07:17

Indentifying Discussed Topics: LDA In Gensim

LDA Introduction 00:02:43

LDA Gensim 00:07:13

LDA pyLDAvis 00:03:54

Toward Machine Reading: Entity Extraction And Linking

Entity Linking 00:03:28

pyspotlight 00:03:16

FRED 00:03:16

Conclusion

Conclusion 00:02:24

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