Deep Learning in Natural Language Processing 1st Edition by Li Deng ISBN 9811052085 9789811052088

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ISBN 10: 9811052085
ISBN 13: 9789811052088
Author: Li Deng

In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence.

This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided.

The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.

Deep Learning in Natural Language Processing 1st Edition Table of contents:

• Chapter 1: Basics of Natural Language Processing

  • Introduction to NLP and its applications
  • Text processing and representation
  • Basic tasks in NLP (e.g., tokenization, parsing, named entity recognition)
  • Traditional vs. deep learning approaches in NLP

• Chapter 2: Introduction to Deep Learning

  • Foundations of deep learning
  • Neural networks and deep architectures
  • Activation functions, loss functions, and optimization
  • Training neural networks: Backpropagation and gradient descent

• Chapter 3: Word Embeddings and Representation Learning

  • Word embeddings: Word2Vec, GloVe, FastText
  • Representation learning and its importance in NLP
  • How embeddings capture semantic relationships in language
  • Transfer learning and pre-trained models

• Chapter 4: Recurrent Neural Networks (RNNs) for NLP

  • Basics of RNNs and their role in sequential data
  • Challenges with vanilla RNNs (e.g., vanishing gradients)
  • Long Short-Term Memory (LSTM) networks and GRUs
  • Applications of RNNs in NLP tasks like language modeling and machine translation

• Chapter 5: Convolutional Neural Networks (CNNs) in NLP

  • Overview of CNNs and their applications
  • CNNs for text classification and sentence modeling
  • Combining CNNs with word embeddings for text representation
  • Use of CNNs in NLP for sentence and document classification

• Chapter 6: Attention Mechanisms and Transformer Models

  • Introduction to attention mechanisms
  • Self-attention and its advantages
  • The Transformer architecture and its applications in NLP
  • Understanding BERT, GPT, and other Transformer-based models

• Chapter 7: Sequence-to-Sequence Models for Machine Translation

  • Sequence-to-sequence models: Encoder-Decoder architecture
  • Applications in machine translation
  • Training sequence models with attention mechanisms
  • Examples of successful machine translation systems

• Chapter 8: Deep Learning for Speech Recognition

  • Deep learning techniques in speech processing
  • Automatic Speech Recognition (ASR) with deep neural networks
  • End-to-end speech recognition models
  • Challenges and solutions in ASR

• Chapter 9: Deep Learning for Sentiment Analysis and Text Classification

  • Text classification using deep learning models
  • Sentiment analysis: Using deep learning to classify emotions and sentiments in text
  • Evaluating deep learning models for sentiment analysis
  • Applications in social media analysis, customer feedback, and opinion mining

• Chapter 10: Deep Learning for Question Answering and Conversational Agents

  • Neural networks for information retrieval and question answering (QA)
  • Building conversational agents with deep learning (e.g., chatbots)
  • Leveraging Transformers for question answering tasks
  • End-to-end QA systems and conversational AI

• Chapter 11: Deep Learning in Text Summarization

  • Techniques for automatic text summarization
  • Abstractive vs. extractive summarization
  • Deep learning methods for summarization: RNNs and Transformer-based models
  • Applications and challenges in text summarization

• Chapter 12: Ethics, Bias, and Fairness in NLP

  • Addressing bias in NLP models and datasets
  • Ethical considerations in deploying deep learning models
  • Ensuring fairness and transparency in AI-based NLP systems
  • Mitigating unintended consequences in automated language processing

• Chapter 13: Future Directions in Deep Learning for NLP

  • Emerging trends and technologies in NLP and deep learning
  • The future of multilingual NLP
  • Integrating deep learning with symbolic AI and knowledge representation
  • Open challenges and opportunities for future research

• Appendices

  • Key mathematical concepts and techniques for deep learning
  • Recommended resources for further learning in deep learning and NLP
  • Tools and libraries for implementing deep learning models in NLP

• References

  • Comprehensive list of academic papers, books, and resources cited throughout the book

• Index

  • An index of key terms, concepts, and technologies covered in the book for easy reference

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