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


Attention is one of the key breakthroughs in machine translation that gave rise to better working NMT systems. Attention allows the decoder to access the full state history of the encoder, leading to creating a richer representation of the source sentence, at the time of translation. Before delving into the details of an attention mechanism, let's understand one of the crucial bottlenecks in our current NMT system and the benefit of attention in dealing with it.

Breaking the context vector bottleneck

As you have probably already guessed, the bottleneck is the context vector, or thought vector, that resides between the encoder and the decoder (see Figure 10.15):

Figure 10.16: The encoder-decoder architecture

To understand why this is a bottleneck, let's imagine translating the following English sentence:

I went to the flower market to buy some flowers

This translates to the following:

Ich ging zum Blumenmarkt, um Blumen zu kaufen

If we are to compress this into a fixed length vector...