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Mastering spaCy

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Getting Started

Introduction to the Course Overview: Getting Started NLP and Python Python String Operations High-level Overview of the spaCy Library Visualization with displaCy Summary: Getting Started Quiz: Getting Started Exercise: Getting Started Solution: Getting Started

Core Operations with spaCy

Overview: Core Operations with spaCy Overview of spaCy Conventions Introducing Tokenization Customizing the Tokenizer and Sentence Segmentation Understanding Lemmatization spaCy Container Objects More spaCy Features Summary: Core Operations with spaCy Quiz: Core Operations with spaCy Exercise: Core Operations with spaCy Solution: Core Operations with spaCy

Linguistic Features

Overview: Linguistic Features

POS Tagging - Continued

Introduction to Dependency Parsing

Introducing NER

Merging and Splitting Tokens

Summary: Linguistic Features

Quiz: Linguistic Features

Exercise: Linguistic Features

Solution: Linguistic Features

Rule-Based Matchmaking

Overview: Rule-Based Matchmaking Token Based Matching Syntax Support and Regex PhraseMatcher and EntityRuler Combining spaCy Models and Matchers Summary: Rule-Based Matchmaking Quiz: Rule-Based Matchmaking Exercise: Rule-Based Matchmaking Solution: Rule-Based Matchmaking

Working with Word Vectors and Semantic Similarity

Overview: Word Vectors and Semantic Similarity Understanding Word Vectors Using spaCy's Pre-trained Vectors The Similarity Method Advanced Semantic Similarity Methods Summary: Word Vectors and Semantic Similarity Quiz: Working with Word Vectors and Semantic Similarity Exercise: Word Vectors and Semantic Similarity Solution: Word Vectors and Semantic Similarity

Putting Everything Together: Semantic Parsing with spaCy

Overview: Semantic Parsing with spaCy Extracting Named Entities Using Dependency Trees For Extracting Entities Using Dependency Relations for Intent Recognition Semantic Similarity Methods for Semantic Parsing Putting It All Together Summary: Semantic Parsing with spaCy Quiz: Semantic Parsing with spaCy Exercise: Semantic Parsing with spaCy Solution: Semantic Parsing with spaCy

Course Assessment

Assessment: spaCy Features

Customizing spaCy Models

Overview: Customizing spaCy Models Getting Started with Data Preparation Updating an Existing Pipeline Component Training a Pipeline Component From Scratch Summary Quiz: Customizing spaCy Models Exercise: Customizing spaCy Models Solution: Customizing spaCy Models

Text Classification with spaCy

Overview: Text Classification with spaCy Understanding the Basics of Text Classification Training the spaCy Text Classifier Sentiment Analysis with spaCy Text classification with spaCy and Keras Embedding Words Summary: Text Classification with SpaCy Quiz: Text Classification with spaCy Exercise: Text Classification with spaCy Solution: Text Classification with spaCy

spaCy and Transformers

Overview: spaCy and Transformers Transformers and Transfer Learning Understanding BERT Transformers and TensorFlow Using BERT for Text Classification Using Transformer Pipelines Transformers and spaCy Summary: spaCy and Transformers Quiz: spaCy and Transformers Exercise: spaCy and Transformers Solution: spaCy and Transformers

Putting Everything Together: Designing a Chatbot with spaCy

Overview: Designing a Chatbot with spaCy Introduction to Conversational AI Getting to Know the Dataset Entity Extraction Intent Recognition Classifying Text with a Character-level LSTM Differentiating Subjects from Objects Anaphora Resolution Summary: Designing a Chatbot with spaCy Quiz: Designing a Chatbot With spaCy

Appendix

Installing spaCy Installing spaCy's Statistical Models More on Visualization with displaCy Saving and loading custom models

Conclusion

Course Assessment

Assessment - Machine Learning with spaCy

Exercise: Linguistic Features

Let's test the concepts we've learned with a coding exercise.

We'll cover the following

Problem

Problem

Write a program using spaCy to perform part-of-speech (POS) tagging, named entity recognition (NER), and dependency parsing on a given text.

Input

A string text representing the text to be processed.

Output

For each token in the text, print its text, its POS tag, and its dependency label, separated by a space.
For each entity in the text, print its text and its NER label, separated by a space.

Attempt the problem in the following code widget:

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