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 sequence-to-sequence learning

Many kinds of problems in machine learning involve transforming an input sequence into an output one. Sequence-to-sequence (seq2seq) learning has proven useful in applications that demand this transformation. For instance, free-form question answering (generating a natural language answer to a natural language question), text summarization, conversational interfaces such as chatbots, and so forth can benefit from seq2seq learning. It is not surprising that MT applications can also exploit this technique to convert a source sequence, such as an English phrase, into the corresponding target sequence, such as an Arabic translation. Seq2seq, pronounced as seek-to-seek, learning falls under the category of neural MT, and unlike solutions based on RBMT and SMT, no domain knowledge of the languages involved is necessary. You can treat the translation problem as the association between input and output tokens of words or characters. Moreover, the translation...