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)


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


abstractive summarization 263

attention mechanism 277, 279

performing 277

transformers 280

transformers, implementing 288

accuracy 54

activation function 153

activation map 336

Adaptive Boosting (AdaBoost) 314-316

agglomerative clustering 398

application programming interface (API) 159

Area Under the ROC Curve (AUC) 59

creating 59, 60

artificial general intelligence (AGI) 4

artificial intelligence (AI) 3, 4

research fields 5-7

artificial narrow intelligence (ANI) 4

artificial neural network (ANN)

creating 154, 155

training 156-159

artificial neuron 153, 154

artificial superintelligence (ASI) 4

association analysis 12

attention mechanism 277, 279

autoencoders 206, 207

autoencoding 346

autoregressive 346