Book Image

Natural Language Processing and Computational Linguistics

By : Bhargav Srinivasa-Desikan
Book Image

Natural Language Processing and Computational Linguistics

By: Bhargav Srinivasa-Desikan

Overview of this book

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.
Table of Contents (22 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Classification with spaCy


While Keras works especially well in standalone text classification tasks, sometimes it might be useful to use Keras in tandem with spaCy, which works exceedingly well in text analysis. In Chapter 3, spaCy's Language Models, Chapter 5, POS-Tagging and Its Applications, Chapter 6, NER-Tagging and Its Applications, and Chapter 7, Dependency Parsing, we already saw how well spaCy works with textual data, and it is no exception when it comes to deep learning – its text oriented approach makes it easy to build a classifier that works well with text. There are two ways to perform text classification with spaCy – one is using its own neural network library, thinc, while the other uses Keras. Both the examples we will explain are from spaCy's documentation, and it is highly recommended that you check out the original examples!

The first example we will be exploring can be found on the spaCy example page, and is titled deep_learning_keras.py [20]. In the example, we use an...