Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow

Machine translation

Humans often communicate with each other by means of a language, compared to other communication methods (for example, gesturing). Currently, more than 5,000 languages are spoken worldwide. Furthermore, learning a language to a level where it is easily understandable for 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 for communicating with different parts of the world. This is where machine translation (MT) comes in. MT systems allow the user to input a sentence in his 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) belonging to a source language S, defined by the following:

Here, .

The source language would be translated to a sentence , where T is the target language and...