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)


This chapter focused on yet another exciting field in natural language processing related to text generation. In this context, we examined chatbots as a convenient case study. In addition, the content included many references to previous chapters to urge you to revisit specific topics from a different perspective.

The power of the transformer architecture and the abundance of data has paved the way for more elaborate language models. We presented how to create such a model from scratch or fine-tune a pre-trained model. During this discussion, we also applied a third type of learning: reinforcement learning.

Evaluation metrics are a constant theme throughout this book; this chapter was no exception. We used perplexity as an evaluation metric and discussed TensorBoard, which helps us shed light on the internal mechanics of deep neural networks. Finally, we worked on creating user interfaces in Python.

The next chapter is the final chapter of this book and deals with...