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)

Introducing Machine Learning for Text

The language phenomenon is still shrouded in mystery despite the recent achievements in various scientific disciplines in terms of understanding how and why it works. Yet, surprisingly, homo sapiens are the only species to develop this complex medium for exchanging information, which has led to the most striking accomplishments of humankind. Although the oral and gestural forms of language were the driving forces over millennia, their written counterpart decisively spread knowledge worldwide. Inspired by the expressive power of human texts, this introductory chapter sets the scene for the discussion in the following chapters, where we examine how to teach machines to extract meaningful interpretations from text corpora.

Building machines that learn from observations is becoming the dominant paradigm due to the ever-increasing amount of data that cannot be processed using traditional methods. For instance, text data is produced in vast quantities...