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
Contributors
Preface
Index

New tasks emerging


Now we will investigate several novel areas that have emerged in the recent past. These areas include detecting sarcasm, language grounding (that is, the process of eliciting common sense from natural language), and skimming text.

Detecting sarcasm

Sarcasm is when a person utters something which actually means the opposite of the utterance (for example, I love Mondays!). Detecting sarcasm can even be difficult for humans sometimes, and detecting sarcasm through NLP is an even harder task. Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation [23], Lotem Peled and Roi Reichart, uses NLP for detecting sarcasm in Twitter posts. They first create a dataset of 3,000 tweet pairs, where one tweet is the sarcastic tweet and the other tweet is the decrypted nonsarcastic tweet. The decrypted tweets were created by five human judges who looked at the tweet and came up with the actual meaning. Then they used a monolingual machine translation mechanism...