Book Image

Machine Learning Techniques for Text

By : Nikos Tsourakis
Book Image

Machine Learning Techniques for Text

By: Nikos Tsourakis

Overview of this book

With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code. A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions. By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.
Table of Contents (13 chapters)

The data explosion

We live in a data-driven world that steadily becomes even more data-driven. The innate tendency of humans to impart information, especially in written form, has caused an abundance of data for various languages and domains. Besides people’s willingness to share information, advances in computer connectivity and storage have paved the way for an explosion in the volume of text data. For instance, hundreds of billions of emails are sent daily, and thousands of tweets are posted per second. Frantically, people and businesses are churning out lots of unstructured data with an increased volume, velocity, and variety, but with less veracity. The four Vs are defining properties of big data and shape our digital world. For that reason, they need some attention:

  • Volume: Big data is about this volume now reaching unprecedented heights. Digital storage has become so cheap and vast in its capacity that we can practically keep all the digital data we’re...