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 hierarchical clustering algorithm

Hierarchical clustering is another unsupervised machine learning algorithm that seeks to build a hierarchy of clusters. To achieve this aim, it constructs a tree-like structure called a dendrogram that shows the hierarchical relationship between objects in a dataset. Typically, there are two ways to construct the dendrogram: the agglomerative clustering approach or the divisive clustering one. The first option is more common and follows a bottom-up approach by sequentially merging similar clusters. In divisive clustering, we put all observations in one big cluster and then successively split the clusters. A top-down approach is adopted in this case. Figure 10.11 shows an example of a dendrogram with the fusions or divisions made at each successive stage:

Figure 10.11 – Hierarchical clustering dendrogram

Next, we examine the basic steps of agglomerative clustering. To facilitate understanding, we reuse...