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

Comparing skip-gram with CBOW

Before looking at the performance differences and investigating reasons, let's remind ourselves about the fundamental difference between the skip-gram and CBOW methods.

As shown in the following figures, given a context and a target word, skip-gram observes only the target word and a single word of the context in a single input/output tuple. However, CBOW observes the target word and all the words in the context in a single sample. For example, if we assume the phrase dog barked at the mailman, skip-gram sees an input-output tuple such as ["dog", "at"] at a single time step, whereas CBOW sees an input-output tuple [["dog","barked","the","mailman"], "at"]. Therefore, in a given batch of data, CBOW receives more information than skip-gram about the context of a given word. Let's next see how this difference affects the performance of the two algorithms.

As shown in the preceding figures, the CBOW model has access to more information (inputs) at a given time compared...