   Natural Language Processing (NLP) with Python spaCy培訓(xùn)

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