Book Image

Machine Learning Techniques for Text

By : Nikos Tsourakis
5 (1)
Book Image

Machine Learning Techniques for Text

5 (1)
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 example-based machine translation

In the era of RBMT systems, it became apparent that a new paradigm in MT was necessary. The reliance on linguistic rules presents many shortcomings. As we saw previously, using a corpus of already-translated examples could serve as a model to base the translation task on. This is the basic idea behind example-based machine translation (EBMT) systems; keep track of well-translated fragments and use this information to facilitate the translation of new sentences. Humans often process short sentences this way; first, they split the source into smaller fragments, then translate the pieces by analogy into previous examples, and, finally, recombine those translations into the target sentence. Deep linguistic analysis is not necessary, and the more examples that are available, the more the translation accuracy improves. Figure 6.14 shows an example:

Figure 6.14 – Using existing translated fragments in MT

The primary...