Hands-on Question Answering Systems with BERT - Applications in Neural Networks and Natural Language Processing
Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you thr...
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Berkeley, CA
Apress, an imprint of Springer Nature
2021
Apress Apress L. P |
| Edition: | 1 |
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| ISBN: | 1484266633, 9781484266632, 9781484266649, 1484266641 |
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| Abstract | Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. |
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| AbstractList | Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.What You Will Learn Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is ForAI and machine learning developers and natural language processing developers. Providing step-by-step guidance and in-depth explanations, this book is a good starting point for developers and data scientists who want to create and design NLP systems using BERT. -- Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT. What You Will Learn * Examine the fundamentals of word embeddings * Apply neural networks and BERT for various NLP tasks * Develop a question-answering system from scratch * Train question-answering systems for your own data Who This Book Is For AI and machine learning developers and natural language processing developers. Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you’ll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you’ll cover word embedding and their types along with the basics of BERT. After this solid foundation, you’ll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You’ll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.What You Will LearnExamine the fundamentals of word embeddingsApply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is For AI and machine learning developers and natural language processing developers. Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. |
| Author | Sabharwal, Navin Agrawal, Amit |
| Author_xml | – sequence: 1 fullname: Sabharwal, Navin – sequence: 2 fullname: Agrawal, Amit |
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| Copyright | 2021 Navin Sabharwal, Amit Agrawal 2021 |
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| Notes | Includes index |
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| Snippet | Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using... Providing step-by-step guidance and in-depth explanations, this book is a good starting point for developers and data scientists who want to create and design... |
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| SubjectTerms | Automatic control Computer Science Data processing General References Machine Learning MATHEMATICS Neural networks (Computer science) Pattern recognition systems Professional and Applied Computing Professional Computing Software Engineering |
| SubjectTermsDisplay | Automatic control -- Data processing. Electronic books. Neural networks (Computer science) Pattern recognition systems -- Data processing. |
| TableOfContents | Title Page
Introduction
Table of Contents
1. Introduction to Natural Language Processing
2. Neural Networks for Natural Language Processing
3. Introduction to Word Embeddings
4. BERT Algorithms Explained
5. BERT Model Applications: Question Answering System
6. BERT Model Applications: Other Tasks
7. Future of BERT Models
Index Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Natural Language Processing -- Natural Language Processing -- Sentence Segmentation -- Tokenization -- Parts of Speech Tagging -- Stemming and Lemmatization -- Identification of Stop Words -- Phrase Extraction -- Named Entity Recognition -- Coreference Resolution -- Bag of Words -- Conclusion -- Chapter 2: Neural Networks for Natural Language Processing -- What Is a Neural Network? -- Building Blocks of Neural Networks -- Neuron -- Input Layer -- Hidden Layers -- Output Layer -- Activation Function -- Neural Network Training -- Types of Neural Networks -- Feed-Forward Neural Networks -- Convolutional Neural Networks -- Recurrent Neural Networks -- Long Short-Term Memory -- Encoders and Decoders -- The Encoder-Decoder Architecture -- Encoder Part of the Model -- Decoder Part of the Model -- Bidirectional Encoders and Decoders -- Transformer Models -- Model Architecture -- Attention Models -- Why Is Attention Required? -- How Attention Works -- Types of Attention Models -- Global Attention Model -- Local Attention Model -- Hard and Soft Attention Model -- Self-Attention Model -- Conclusion -- Chapter 3: Introduction to Word Embeddings -- One-Hot Representation -- Count Vector -- TF-IDF Vectorization -- What Is Word Embedding? -- Different Methods of Word Embedding -- Word2vec -- Continuous Bag of Words -- Skip Gram Model -- GloVe -- Sentence Embeddings -- ELMo -- Universal Sentence Encoder -- Bidirectional Encoder Representations from Transformers -- BERT Base Model -- BERT Large Model -- Conclusion -- Chapter 4: BERT Algorithms Explained -- How Does BERT Work? -- Text Processing -- Masked Language Modeling -- Next Sentence Prediction -- Text Classification Using BERT -- Benchmarks for BERT Model GLUE Benchmark -- SQuAD Dataset -- IMDB Reviews Dataset -- RACE Benchmark -- Types of BERT-Based Models -- ALBERT -- RoBERTa -- DistilBERT -- Distillation Loss -- Cosine Embedding Loss -- Masked Language Modeling Loss -- Architectural Modifications -- StructBERT -- Structural Pretraining in StructBERT -- Pretraining Objectives -- BERTjoint for Natural Questions -- Conclusion -- Chapter 5: BERT Model Applications: Question Answering System -- Types of QA Systems -- Question Answering System Design Using BERT -- For Windows Server -- Creation of REST API -- For Linux Server -- Creation of REST API -- Open-Domain Question Answering System -- Model Architecture -- DeepPavlov QA System -- Conclusion -- Chapter 6: BERT Model Applications: Other Tasks -- Sentiment Analysis -- Named Entity Recognition -- Text Classification -- Text Summarization -- Conclusion -- Chapter 7: Future of BERT Models -- Future Capabilities -- Abstractive Summarization -- Natural Language Generation -- Machine Translation -- Conclusion -- Index |
| Title | Hands-on Question Answering Systems with BERT - Applications in Neural Networks and Natural Language Processing |
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