Book Image

Natural Language Processing with TensorFlow - Second Edition

By : Thushan Ganegedara
2 (1)
Book Image

Natural Language Processing with TensorFlow - Second Edition

2 (1)
By: Thushan Ganegedara

Overview of this book

Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures. The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP. TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models. By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
Table of Contents (15 chapters)
12
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13
Index

Machine translation

Humans often communicate with each other by means of a language, compared to other communication methods (for example, gesturing). Currently, more than 6,000 languages are spoken worldwide. Furthermore, learning a language to a level where it is easily understandable to a native speaker of that language is a difficult task to master. However, communication is essential for sharing knowledge, socializing, and expanding your network. Therefore, language acts as a barrier to communicating with people in different parts of the world. This is where Machine Translation (MT) comes in. MT systems allow the user to input a sentence in their own tongue (known as the source language) and output a sentence in a desired target language.

The problem with MT can be formulated as follows. Say we are given a sentence (or a sequence of words) Ws belonging to a source language S, defined by the following:

Here, .

The source language would be translated to a sentence...