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
Contributors
Preface
Index

Summary


In this chapter, we examined the performance difference between the skip-gram and CBOW algorithms. For the comparison, we used a popular two-dimensional visualization technique, t-SNE, which we also briefly introduced to you, touching on the fundamental intuition and mathematics behind the method.

Next, we introduced you to the several extensions to Word2vec algorithms that boost their performance, followed by several novel algorithms that were based on the skip-gram and CBOW algorithms. Structured skip-gram extends the skip-gram algorithm by preserving the position of the context word during optimization, allowing the algorithm to treat input-output based on the distance between them. The same extension can be applied to the CBOW algorithm, and this results in the continuous window algorithm.

Then we discussed GloVe—another word embedding learning technique. GloVe takes the current Word2vec algorithms a step further by incorporating global statistics into the optimization, thus increasing...