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)
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Overview of conversational agents

A conversational agent interacts with people using speech or text. Facebook Messenger would be an example of a text-based agent while Alexa and Siri are examples of agents that interact through speech. In either case, the agent needs to understand the user's intent and respond accordingly. Hence, the core part of the agent would be a natural language understanding (NLU) module. This module would interface with a natural language generation (NLG) module to supply a response back to the user. Voice agents differ from text-based agents in having an additional module that converts voice to text and vice versa. We can imagine the system having the following logical structure for a voice-activated agent:

Figure 9.1: Conceptual architecture of a conversational AI system

The main difference between a speech-based system and a text-based system is how the users communicate with the system. All the other parts to the right of the Speech Recognition...