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

Classical approaches to learning word representation

In this section, we will discuss some of the classical approaches used for numerically representing words. These approaches mainly can be categorized into two classes: approaches that use external resources for representing words and approaches that do not. First, we will discuss WordNet—one of the most popular external resource-based approaches for representing words. Then we will proceed to more localized methods (that is, those that do not rely on external resources), such as one-hot encoding and Term Frequency-Inverse Document Frequency (TF-IDF).

WordNet – using an external lexical knowledge base for learning word representations

WordNet is one of the most popular classical approaches or statistical NLP that deals with word representations. It relies on an external lexical knowledge base that encodes the information about the definition, synonyms, ancestors, descendants, and so forth of a given word. WordNet allows a user to infer various...