Book Image

Mastering spaCy

By : Duygu Altınok
Book Image

Mastering spaCy

By: Duygu Altınok

Overview of this book

spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications. You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results. By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.
Table of Contents (15 chapters)
1
Section 1: Getting Started with spaCy
4
Section 2: spaCy Features
9
Section 3: Machine Learning with spaCy

Summary

In this chapter, we explored how to customize spaCy statistical models according to our own domain and data. First, we learned the key points of deciding whether we really need custom model training. Then, we went through an essential part of statistical algorithm design – data collection, and labeling.

Here we also learned about two annotation tools – Prodigy and Brat. Next, we started model training by updating spaCy's NER component with our navigation domain data samples. We learned the necessary model training steps, including disabling the other pipeline components, creating example objects to hold our examples, and feeding our examples to the training code.

Finally, we learned how to train an NER model from scratch on a small toy dataset and on a real medical domain dataset.

With this chapter, we took a step into the statistical NLP playground. In the next chapter, we will take more steps in statistical modeling and learn about text classification...