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

Summary

With Chapter 8, Topic Models and Chapter 9, Advanced Topic Modelling, we are now equipped with the tools and knowledge of applying topic models to our textual data. Topic modelling is a largely data exploratory tool, but we can also carry out some more targeted analysis, like seeing the topics which make up a document, or which words in a document belong to which topic. Gensim gives us the functionality to carry out these tasks quite easily, with its API constructed so that we can access the mathematical information behind topic models without a hassle.

In the next chapter, we will carry our more targeted text analysis tasks, such as clustering or classification. Clustering and classification algorithms are largely used in text analysis to group similar documents together and are machine learning algorithms. We will explain the intuition behind these methods as well as...