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)

Evaluation of the results

Determining the value or worth of something in terms of quantity and quality is the process of evaluation. The increasing sophistication of text systems necessitates evaluation frameworks that measure the stated objectives and anticipated results. These frameworks serve a dual role – assessing different versions of the same product and also comparing similar systems. The topic of evaluation has grown into an essential part of systems development and a research field of its own.

Numerous convenient methods have been put forth to evaluate ML systems, which frequently make use of various computer- and human-centered metrics, most commonly known as objective and subjective evaluation. For example, using objective metrics allows us to measure something consistently and typically defies interpretation; either the spam detector achieved an accuracy above a threshold or didn’t. On the other hand, subjective evaluations are more expensive and time...