Algorithms and Practice Zhu

Algorithms and Practice Zhu – Ebook Instant Download/Delivery ISBN(s): 9780323901185,0323901182,9780323901192,0323901190

Product details:

• ISBN 10: 0323901190
• ISBN 13: 9780323901192
• Author: Chenguang Zhu

Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing.

Table contents:

Part I: Foundation

Chapter 1. Introduction to machine reading comprehension

1.1 The machine reading comprehension task

1.2 Natural language processing

1.3 Deep learning

1.4 Evaluation of machine reading comprehension

1.5 Machine reading comprehension datasets

1.6 How to make an machine reading comprehension dataset

1.7 Summary

Chapter 2. The basics of natural language processing

2.1 Tokenization

2.2 The cornerstone of natural language processing: word vectors

2.3 Linguistic tagging

2.4 Language model

2.5 Summary

Chapter 3. Deep learning in natural language processing

3.1 From word vector to text vector

3.2 Answer multiple-choice questions: natural language understanding

3.3 Write an article: natural language generation

3.4 Keep focused: attention mechanism

3.5 Summary

Part II: Architecture

Chapter 4. Architecture of machine reading comprehension models

4.1 General architecture of machine reading comprehension models

4.2 Encoding layer

4.3 Interaction layer

4.4 Output layer

4.5 Summary

Chapter 5. Common machine reading comprehension models

5.1 Bidirectional attention flow model

5.2 R-NET

5.3 FusionNet

5.4 Essential-term-aware retriever–reader

5.5 Summary

Chapter 6. Pretrained language models

6.1 Pretrained models and transfer learning

6.2 Translation-based pretrained language model: CoVe

6.3 Pretrained language model ELMo

6.4 The generative pretraining language model: generative pre-training (GPT)

6.5 The phenomenal pretrained language model: BERT

6.6 Summary

Part III: Application

Chapter 7. Code analysis of the SDNet model

7.1 Multiturn conversational machine reading comprehension model: SDNet

7.2 Introduction to code

7.3 Preprocessing

7.4 Training

7.5 Batch generator

7.6 SDNet model

7.7 Summary

Chapter 8. Applications and future of machine reading comprehension

8.1 Intelligent customer service

8.2 Search engine

8.3 Health care

8.4 Laws

8.5 Finance

8.6 Education

8.7 The future of machine reading comprehension

8.8 Summary

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