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

More recent algorithms extending skip-gram and CBOW

We already saw that the Word2vec techniques are quite powerful in capturing semantics of words. However, they are not without their limitations. For example, they do not pay attention to the distance between a context word and the target word. However, if the context word is further away from the target word, its impact on the target word should be less. Therefore, we will discuss techniques that pay separate attention to different positions in the context. Another limitation of Word2vec is that it only pays attention to a very small window around a given word when computing the word vector. However, in reality, the way the word co-occurs throughout a corpus should be considered to compute good word vectors. So, we will look at a technique that not only looks at the context of a word, but also at the global co-occurrence information of the word.

A limitation of the skip-gram algorithm

The previously-discussed skip-gram algorithm and all its...