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

Preparing data for the NMT system

In this section, we will talk about the exact process for preparing data for training and predicting from the NMT system. First, we talk will about how to prepare training data (that is, the source sentence and target sentence pairs) to train the NMT system followed by inputting a given source sentence to produce the translation of the source sentence.

At training time

The training data consists of pairs of source sentences and corresponding translations to the target language. An example might look like this:

  • ( Ich ging nach Hause , I went home)

  • ( Sie hat in der Schule gewartet , She was waiting at school)

We have N such pairs in our dataset. If we are to implement a fairly good translator, N needs to be in the scale of millions. An increase of training data as such, also implies prolonged training times.

Next, we will introduce two special tokens: <s> and </s>. The <s> token represents the start of a sentence, whereas </s> represents...