Book Image

Advanced Natural Language Processing with TensorFlow 2

By : Ashish Bansal, Tony Mullen
Book Image

Advanced Natural Language Processing with TensorFlow 2

By: Ashish Bansal, Tony Mullen

Overview of this book

Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
Table of Contents (13 chapters)
Other Books You May Enjoy

Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks

"A picture is worth a thousand words" is a famous adage. In this chapter, we'll put this adage to the test and generate captions for an image. In doing so, we'll work with multi-modal networks. Thus far, we have operated on text as input. Humans can handle multiple sensory inputs together to make sense of the environment around them. We can watch a video with subtitles and combine the information provided to understand the scene. We can use facial expressions and lip movement along with sounds to understand speech. We can recognize text in an image, and we can answer natural language questions about images. In other words, we have the ability to process information from different modalities at the same time, and then put them together to understand the world around us. The future of artificial intelligence and deep learning is in building multi-modal networks as they closely...