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Advanced Natural Language Processing with TensorFlow 2
Advanced Natural Language Processing with TensorFlow 2

Advanced Natural Language Processing with TensorFlow 2: Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more

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Advanced Natural Language Processing with TensorFlow 2

Understanding Sentiment in Natural Language with BiLSTMs

Natural Language Understanding (NLU) is a significant subfield of Natural Language Processing (NLP). In the last decade, there has been a resurgence of interest in this field with the dramatic success of chatbots such as Amazon's Alexa and Apple's Siri. This chapter will introduce the broad area of NLU and its main applications.

Specific model architectures called Recurrent Neural Networks (RNNs), with special units called Long Short-Term Memory (LSTM) units, have been developed to make the task of understanding natural language easier. LSTMs in NLP are analogous to convolution blocks in computer vision. We will take two examples to build models that can understand natural language. Our first example is understanding the sentiment of movie reviews. This will be the focus of this chapter. The other example is one of the fundamental building blocks of NLU, Named Entity Recognition (NER). That will be the...

Natural language understanding

NLU enables the processing of unstructured text and extracts meaning and critical pieces of information that are actionable. Enabling a computer to understand sentences of text is a very hard challenge. One aspect of NLU is understanding the meaning of sentences. Sentiment analysis of a sentence becomes possible after understanding the sentence. Another useful application is the classification of sentences to a topic. This topic classification can also help in the disambiguation of entities. Consider the following sentence: "A CNN helps improve the accuracy of object recognition." Without understanding that this sentence is about machine learning, an incorrect inference may be made about the entity CNN. It may be interpreted as the news organization as opposed to a deep learning architecture used in computer vision. An example of a sentiment analysis model is built using a specific RNN architecture called BiLSTMs later in this chapter.

...

Bi-directional LSTMs – BiLSTMs

LSTMs are one of the styles of recurrent neural networks, or RNNs. RNNs are built to handle sequences and learn the structure of them. An RNN does that by using the output generated after processing the previous item in the sequence along with the current item to generate the next output.

Mathematically, this can be expressed like so:

This equation says that to compute the output at time t, the output at t-1 is used as an input along with the input data xt at the same time step. Along with this, a set of parameters or learned weights, represented by , are also used in computing the output. The objective of training an RNN is to learn these weights This particular formulation of an RNN is unique. In previous examples, we have not used the output of a batch to determine the output of a future batch. While we focus on applications of RNNs on language where a sentence is modeled as a sequence of words appearing one after the other, RNNs...

Summary

This is a foundational chapter in our journey through advanced NLP problems. Many advanced models use building blocks such as BiRNNs. First, we used the TensorFlow Datasets package to load data. Our work of building a vocabulary, tokenizer, and encoder for vectorization was simplified through the use of this library. After understanding LSTMs and BiLSTMs, we built models to do sentiment analysis. Our work showed promise but was far away from the state-of-the-art results, which will be addressed in future chapters. However, we are now armed with the fundamental building blocks that will enable us to tackle more challenging problems.

Armed with this knowledge of LSTMs, we are ready to build our first NER model using BiLSTMs in the next chapter. Once this model is built, we will try to improve it using CRFs and Viterbi decoding.

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Key benefits

  • Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2
  • Explore applications like text generation, summarization, weakly supervised labelling and more
  • Read cutting edge material with seminal papers provided in the GitHub repository with full working code

Description

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.

Who is this book for?

This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.

What you will learn

  • Grasp important pre-steps in building NLP applications like POS tagging
  • Use transfer and weakly supervised learning using libraries like Snorkel
  • Do sentiment analysis using BERT
  • Apply encoder-decoder NN architectures and beam search for summarizing texts
  • Use Transformer models with attention to bring images and text together
  • Build apps that generate captions and answer questions about images using custom Transformers
  • Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models
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Publication date : Feb 04, 2021
Length: 380 pages
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Language : English
ISBN-13 : 9781800200937
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Length: 380 pages
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Language : English
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Table of Contents

12 Chapters
Essentials of NLP Chevron down icon Chevron up icon
Understanding Sentiment in Natural Language with BiLSTMs Chevron down icon Chevron up icon
Named Entity Recognition (NER) with BiLSTMs, CRFs, and Viterbi Decoding Chevron down icon Chevron up icon
Transfer Learning with BERT Chevron down icon Chevron up icon
Generating Text with RNNs and GPT-2 Chevron down icon Chevron up icon
Text Summarization with Seq2seq Attention and Transformer Networks Chevron down icon Chevron up icon
Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks Chevron down icon Chevron up icon
Weakly Supervised Learning for Classification with Snorkel Chevron down icon Chevron up icon
Building Conversational AI Applications with Deep Learning Chevron down icon Chevron up icon
Installation and Setup Instructions for Code Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(35 Ratings)
5 star 88.6%
4 star 5.7%
3 star 2.9%
2 star 2.9%
1 star 0%
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Noopur Sethi Jun 06, 2021
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Easy read - very helpful
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NJ Jun 05, 2021
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This is a great book for everyone to read who want to pursue NLP using TensorFlow.
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Tushar Kant Apr 10, 2021
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I had a chance to read this book on NLP (Natural Language Processing) by Ashish Bansal. The most unique feature of this book is that it is a rare combination of deep theoretical insights (supported by both seminal and state-of-art papers in the domain) and practical implementation with code that may be easily downloaded from Github. The book may be used as primary source of knowledge as well as reference by people across industry & academia.I have personally learnt a lot from the chapters related to semi-supervised learning using GPT, Transformers etc. as well as Multi-Modal Networks. The beauty of the book lies in it's simplicity & eloquence in explaining very complex concepts in a simple & easy to understand manner. Our best wishes to Ashish for the success of this book and hope that the book becomes a de facto reference from industry professionals and teaching guide for people in academics.
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Sanjib Basu Feb 20, 2021
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If you are looking for an advanced book on NLP, full of examples and codes, please try this book. Starting from basic concepts of algorithms, deep learning, to using the latest libraries, advanced techniques, the book proves to be very useful. The descriptions are to the point, carrying little fuss on personal opinions, decorative illustrations, and literal analogies. In other words, the book is concise to compress the vast field of NLP knowledge.
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Manu Goyal Jun 02, 2021
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A practical book for techniques in NLP.The best part is the sample code included with the book that makes it easy to test.A word of caution - not very suitable for beginners.Overall 5*!
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