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 DBSCAN

The basic idea behind the density-based spatial clustering of applications with noise (DBSCAN) algorithm is that clusters are regions of high point density, separated from other clusters by low point density regions. The algorithm takes each point in the dataset to identify the high-density regions and checks whether its neighborhood contains a minimum number of points. Unlike K-means, DBSCAN does not require manually specifying the number of clusters; it is more immune to outliers and more appropriate when the clusters have complex shapes.

To employ the algorithm, we need to set two hyperparameters:

  • epsilon is the radius of the circle to be created around each point to check the region’s density
  • minPts determines the minimum number of data points within the circle to label its center as a core point

All the data points with less than minPts but more than one point in their neighborhood are called border points. Finally, data points without...