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

Deep learning

Throughout this book, we have made use of machine learning techniques, with topic modeling, clustering and classifying algorithms, as well as what we call shallow learning word embeddings. Word embeddings were our first glimpse into neural networks and the kind of semantic information they can learn.

Neural networks can be understood as a computing system or machine learning algorithm whose architecture is vaguely inspired by biological neurons in the brain. We say vaguely here because of the lack of thorough understanding we have of the human brain through the neural connections and structure of the brain was certainly influential in some of the basic building blocks of neural networks, such as the perceptron [1] and single-layer neural network [2].

A neural network generally consists of a number of nodes that perform mathematical operations and...