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

Current trends in NLP

In this section, we will talk about current trends in NLP. These trends are from the NLP research conducted between 2012 and early 2018. First let's talk about the current states of word embeddings. Word embeddings is a crucial topic as we have already seen many interesting tasks that rely on word embeddings to perform well. We will then look at important improvements in NMT.

Word embeddings

Many variants of word embeddings have emerged over time. With the inception of high-quality word embeddings (refer to Distributed representations of words and phrases and their compositionality, Mikolov and others [1]) in NLP, it can be said that NLP had a resurgence, where many took an interest in using word embeddings in various NLP tasks (for example, sentiment analysis, machine translation, and question answering). Also, there have been many attempts to improve word embeddings, leading to even better embeddings. The four models that we'll introduce are in the areas of region embedding...