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

A typical text processing workflow

To understand how to process text, it is important to understand the general workflow for NLP. The following diagram illustrates the basic steps:

Figure 1.1: Typical stages of a text processing workflow

The first two steps of the process in the preceding diagram involve collecting labeled data. A supervised model or even a semi-supervised model needs data to operate. The next step is usually normalizing and featurizing the data. Models have a hard time processing text data as is. There is a lot of hidden structure in a given text that needs to be processed and exposed. These two steps focus on that. The last step is building a model with the processed inputs. While NLP has some unique models, this chapter will use only a simple deep neural network and focus more on the normalization and vectorization/featurization. Often, the last three stages operate in a cycle, even though the diagram may give the impression of linearity. In industry, additional features require more effort to develop and more resources to keep running. Hence, it is important that features add value. Taking this approach, we will use a simple model to validate different normalization/vectorization/featurization steps. Now, let's look at each of these stages in detail.