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 deep learning

The dictionary definition of "modality" states that it is "a particular mode in which something exists or is experienced or expressed." Sensory modalities, like touch, taste, smell, vision, and sound, allow humans to experience the world around them. Suppose you are out at the farm picking strawberries, and your friend tells you to pick ripe and red strawberries. The instruction, ripe and red strawberries, is processed and converted into a visual and haptic criterion. As you see strawberries and feel them, you know instinctively if they match the criteria of ripe and red. This task is an example of multiple modalities working together for a task. As you can imagine, these capabilities are essential for robotics.

As a direct application of the preceding example, consider a harvesting robot that needs to pick ripe and ready fruit. In December 1976, Harry McGurk and John MacDonald published a piece of research titled Hearing lips and...