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)

Understanding language modeling

Language models are key ingredients for creating chatbots and many natural language processing applications. In the Modeling the translation problem section of Chapter 6, Teaching Machines to Translate, we stated that a language model expresses our confidence that a sentence is probable in the target language. Probability in this context does not necessarily refer to whether a sentence is grammatically correct but how it resembles how people write. Essentially, a language model learns from text resources, which can contain ungrammatical sentences, misspelled words, slang, biases, and so forth. Therefore, it is a probability distribution over words or word sequences derived from the training corpus.

In simple terms, the objective is to predict the next word, given all previous words within some text. A familiar example is the autocomplete feature in Google’s search bar, which allows you to construct search queries. In this chapter, we will revisit...