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

The Continuous Bag-of-Words algorithm

The CBOW model has a working similar to the skip-gram algorithm with one significant change in the problem formulation. In the skip-gram model, we predicted the context words from the target word. However, in the CBOW model, we will predict the target from contextual words. Let's compare what data looks like for skip-gram and CBOW by taking the previous example sentence:

The dog barked at the mailman.

For skip-gram, data tuples—(input word, output word)—might look like this:

(dog, the), (dog, barked), (barked, dog), and so on.

For CBOW, data tuples would look like the following:

([the, barked], dog), ([dog, at], barked), and so on.

Consequently, the input of the CBOW has a dimensionality of 2 × m × D, where m is the context window size and D is the dimensionality of the embeddings. The conceptual model of CBOW is shown in Figure 3.13:

Figure 3.13: The CBOW model

We will not go into great details about the intricacies of CBOW as they are quite similar to...