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)

Understanding text generation

Our access to various services gradually evolves to become technology-driven rather than human-driven. Try to think of the last time you contacted the call center of a company, where an automated system probably answered your call. Replacing the human factor presents many competitive advantages in terms of cost and availability. However, these systems do not fully incorporate the communicative behaviors humans use and therefore are limited in reaching their full potential. The effort, in any case, is to create machines that are increasingly adept at sounding human and can pass the Turing test, which has long been a benchmark for machine intelligence.

Interesting fact

In 1950, the ingenious computer scientist Alan Turing introduced a test to check whether a machine can consistently fool an interviewer into believing it is a human. Today, the test refers to a more general behavioral benchmark for the presence of intelligence.

Natural language generation...