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

Other applications of Seq2Seq models – chatbots

One other popular application of sequence to sequence models is in creating chatbots. A chatbot is a computer program that is able to make a realistic conversation with a human. Such applications are very useful for companies with a huge customer base. Responding to the customers asking basic questions for which answers are obvious accounts for a significant portion of customer support requests. A chatbot can serve customers with basic concerns when it is able to find an answer. Also, if the chatbot is unable to answer a question, the request gets redirected to a human operator. Chatbots can save lot of the time that human operators spend answering basic concerns and let them attend to more difficult tasks.

Training a chatbot

So, how can we use a sequence-to-sequence model to train a chatbot? The answer is quite straightforward as we have already learned about the machine translation model. The only difference would be how the source and target...