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 the k-nearest neighbors algorithm

This section deals with a classification algorithm that is very easy to understand intuitively through an example. Consider the cloud in Figure 3.11 that contains three types of smiley faces – happy, sad, and neutral. There is also a hidden face depicted by a question mark. If you had to guess what its actual type was, what would that be?

Figure 3.11 – A cloud with happy, sad, and neutral smiley faces

Most probably, it’s a happy face. Right? The implicit assumption is that one needs to examine the neighborhood to identify the hidden type. As more happy faces are nearby, we can reasonably argue that the face shows a happy one.

This line of thought is precisely the intuition behind the k-nearest neighbors (KNN) algorithm. KNN is a non-parametric and lazy learning method that stores the position of all data samples and classifies new cases based on some similarity measure. Lazy learning means...