Natural Language Processing (NLP) with Python spaCy

Introduction

• Defining "Industrial-Strength Natural Language Processing"

Installing spaCy

spaCy Components

• Part-of-speech tagger
• Named entity recognizer
• Dependency parser

Overview of spaCy Features and Syntax

Understanding spaCy Modeling

• Statistical modeling and prediction

Using the SpaCy Command Line Interface (CLI)

• Basic commands

Creating a Simple Application to Predict Behavior

Training a New Statistical Model

• Data (for training)
• Labels (tags, named entities, etc.)

Loading the Model

• Shuffling and looping

Saving the Model

Providing Feedback to the Model

• Error gradient

Updating the Model

• Updating the entity recognizer
• Extracting tokens with rule-based matcher

Developing a Generalized Theory for Expected Outcomes

Case Study

• Distinguishing Product Names from Company Names

Refining the Training Data

• Selecting representative data
• Setting the dropout rate

Other Training Styles

• Passing raw texts
• Passing dictionaries of annotations

Using spaCy to Pre-process Text for Deep Learning

Integrating spaCy with Legacy Applications

Testing and Debugging the spaCy Model

• The importance of iteration

Deploying the Model to Production

Monitoring and Adjusting the Model

Troubleshooting

Summary and Conclusion