Representation Learning for Natural Language Processing.

Representation Learning for Natural Language Processing.

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Bibliographic Details
:
Liu, Zhiyuan.
TeilnehmendeR:
Lin, Yankai.
Sun, Maosong.
Place / Publishing House:Singapore : : Springer,, 2023.
Ã2023.
Year of Publication:2023
Edition:2nd ed.
Language:English
Online Access:
Physical Description:1 online resource (535 pages)
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Table of Contents:
  • Intro
  • Preface
  • Book Organization
  • Book Cover
  • Note for the Second Edition
  • Prerequisites
  • Contact Information
  • Acknowledgments
  • Acknowledgments for the Second Edition
  • Acknowledgments for the First Edition
  • Contents
  • Contributors
  • Acronyms
  • Symbols and Notations
  • 1 Representation Learning and NLP
  • 1.1 Motivation
  • 1.2 Why Representation Learning Is Important for NLP
  • 1.2.1 Multiple Granularities
  • 1.2.2 Multiple Knowledge
  • 1.2.3 Multiple Tasks
  • 1.2.4 Multiple Domains
  • 1.3 Development of Representation Learning for NLP
  • 1.3.1 Symbolic Representation and Statistical Learning
  • 1.3.2 Distributed Representation and Deep Learning
  • 1.3.3 Going Deeper and Larger with Pre-training on Big Data
  • 1.4 Intellectual Origins of Distributed Representation
  • 1.4.1 Representation Debates in Cognitive Neuroscience
  • 1.4.2 Knowledge Representation in AI
  • 1.4.3 Feature Engineering in Machine Learning
  • 1.4.4 Linguistics
  • 1.5 Representation Learning Approaches in NLP
  • 1.5.1 Feature Engineering
  • 1.5.2 Supervised Representation Learning
  • 1.5.3 Self-supervised Representation Learning
  • 1.6 How to Apply Representation Learning to NLP
  • 1.6.1 Input Augmentation
  • 1.6.2 Architecture Reformulation
  • 1.6.3 Objective Regularization
  • 1.6.4 Parameter Transfer
  • 1.7 Advantages of Distributed Representation Learning
  • 1.8 The Organization of This Book
  • References
  • 2 Word Representation Learning
  • 2.1 Introduction
  • 2.2 Symbolic Word Representation
  • 2.2.1 One-Hot Word Representation
  • 2.2.2 Linguistic KB-based Word Representation
  • 2.2.3 Corpus-based Word Representation
  • 2.3 Distributed Word Representation
  • 2.3.1 Preliminary: Interpreting the Representation
  • 2.3.2 Matrix Factorization-based Word Representation
  • 2.3.3 Word2vec and GloVe
  • 2.3.4 Contextualized Word Representation.
  • 2.4 Advanced Topics
  • 2.4.1 Informative Word Representation
  • 2.4.2 Interpretable Word Representation
  • 2.5 Applications
  • 2.5.1 NLP
  • 2.5.2 Cognitive Psychology
  • 2.5.3 History and Social Science
  • 2.6 Summary and Further Readings
  • References
  • 3 Representation Learning for Compositional Semantics
  • 3.1 Introduction
  • 3.2 Binary Composition
  • 3.2.1 Additive Model
  • 3.2.2 Multiplicative Model
  • 3.3 N-ary Composition
  • 3.4 Summary and Further Readings
  • References
  • 4 Sentence and Document Representation Learning
  • 4.1 Introduction
  • 4.2 Symbolic Sentence Representation
  • 4.2.1 Bag-of-Words Model
  • 4.2.2 Probabilistic Language Model
  • 4.3 Neural Language Models
  • 4.3.1 Feed-Forward Neural Network
  • 4.3.2 Convolutional Neural Network
  • 4.3.3 Recurrent Neural Network
  • 4.3.4 Transformer
  • 4.3.5 Enhancing Neural Language Models
  • 4.4 From Sentence to Document Representation
  • 4.4.1 Memory-Based Document Representation
  • 4.4.2 Hierarchical Document Representation
  • 4.5 Applications
  • 4.5.1 Text Classification
  • 4.5.2 Information Retrieval
  • 4.5.3 Reading Comprehension
  • 4.5.4 Open-Domain Question Answering
  • 4.5.5 Sequence Labeling
  • 4.5.6 Sequence-to-Sequence Generation
  • 4.6 Summary and Further Readings
  • References
  • 5 Pre-trained Models for Representation Learning
  • 5.1 Introduction
  • 5.2 Pre-training Tasks
  • 5.2.1 Word-Level Pre-training
  • 5.2.2 Sentence-Level Pre-training
  • 5.3 Model Adaptation
  • 5.3.1 Full-Parameter Fine-Tuning
  • 5.3.2 Delta Tuning
  • 5.3.3 Prompt Learning
  • 5.4 Advanced Topics
  • 5.4.1 Better Model Architecture
  • 5.4.2 Multilingual Representation
  • 5.4.3 Multi-Task Representation
  • 5.4.4 Efficient Representation
  • 5.4.5 Chain-of-Thought Reasoning
  • 5.5 Summary and Further Readings
  • References
  • 6 Graph Representation Learning
  • 6.1 Introduction.
  • 6.2 Symbolic Graph Representation
  • 6.3 Shallow Node Representation Learning
  • 6.3.1 Spectral Clustering
  • 6.3.2 Shallow Neural Networks
  • 6.3.3 Matrix Factorization
  • 6.4 Deep Node Representation Learning
  • 6.4.1 Autoencoder-Based Methods
  • 6.4.2 Graph Convolutional Networks
  • 6.4.3 Graph Attention Networks
  • 6.4.4 Graph Recurrent Networks
  • 6.4.5 Graph Transformers
  • 6.4.6 Extensions
  • 6.5 From Node Representation to Graph Representation
  • 6.5.1 Flat Pooling
  • 6.5.2 Hierarchical Pooling
  • 6.6 Self-Supervised Graph Representation Learning
  • 6.7 Applications
  • 6.8 Summary and Further Readings
  • References
  • 7 Cross-Modal Representation Learning
  • 7.1 Introduction
  • 7.2 Cross-Modal Capabilities
  • 7.3 Shallow Cross-Modal Representation Learning
  • 7.4 Deep Cross-Modal Representation Learning
  • 7.4.1 Cross-Modal Understanding
  • 7.4.2 Cross-Modal Retrieval
  • 7.4.3 Cross-Modal Generation
  • 7.5 Deep Cross-Modal Pre-training
  • 7.5.1 Input Representations
  • 7.5.2 Model Architectures
  • 7.5.3 Pre-training Tasks
  • 7.5.4 Adaptation Approaches
  • 7.6 Applications
  • 7.7 Summary and Further Readings
  • References
  • 8 Robust Representation Learning
  • 8.1 Introduction
  • 8.2 Backdoor Robustness
  • 8.2.1 Backdoor Attack on Supervised Representation Learning
  • 8.2.2 Backdoor Attack on Self-Supervised Representation Learning
  • 8.2.3 Backdoor Defense
  • 8.2.4 Toolkits
  • 8.3 Adversarial Robustness
  • 8.3.1 Adversarial Attack
  • 8.3.2 Adversarial Defense
  • 8.3.3 Toolkits
  • 8.4 Out-of-Distribution Robustness
  • 8.4.1 Spurious Correlation
  • 8.4.2 Domain Shift
  • 8.4.3 Subpopulation Shift
  • 8.5 Interpretability
  • 8.5.1 Understanding Model Functionality
  • 8.5.2 Explaining Model Mechanism
  • 8.6 Summary and Further Readings
  • References
  • 9 Knowledge Representation Learning and Knowledge-Guided NLP
  • 9.1 Introduction.
  • 9.2 Symbolic Knowledge and Model Knowledge
  • 9.2.1 Symbolic Knowledge
  • 9.2.2 Model Knowledge
  • 9.2.3 Integrating Symbolic Knowledge and Model Knowledge
  • 9.3 Knowledge Representation Learning
  • 9.3.1 Linear Representation
  • 9.3.2 Translation Representation
  • 9.3.3 Neural Representation
  • 9.3.4 Manifold Representation
  • 9.3.5 Contextualized Representation
  • 9.3.6 Summary
  • 9.4 Knowledge-Guided NLP
  • 9.4.1 Knowledge Augmentation
  • 9.4.2 Knowledge Reformulation
  • 9.4.3 Knowledge Regularization
  • 9.4.4 Knowledge Transfer
  • 9.4.5 Summary
  • 9.5 Knowledge Acquisition
  • 9.5.1 Sentence-Level Relation Extraction
  • 9.5.2 Bag-Level Relation Extraction
  • 9.5.3 Document-Level Relation Extraction
  • 9.5.4 Few-Shot Relation Extraction
  • 9.5.5 Open-Domain Relation Extraction
  • 9.5.6 Contextualized Relation Extraction
  • 9.5.7 Summary
  • 9.6 Summary and Further Readings
  • References
  • 10 Sememe-Based Lexical Knowledge Representation Learning
  • 10.1 Introduction
  • 10.2 Linguistic and Commonsense Knowledge Bases
  • 10.2.1 WordNet and ConceptNet
  • 10.2.2 HowNet
  • 10.2.3 HowNet and Deep Learning
  • 10.3 Sememe Knowledge Representation
  • 10.3.1 Sememe-Encoded Word Representation
  • 10.3.2 Sememe-Regularized Word Representation
  • 10.4 Sememe-Guided Natural Language Processing
  • 10.4.1 Sememe-Guided Semantic Compositionality Modeling
  • 10.4.2 Sememe-Guided Language Modeling
  • 10.4.3 Sememe-Guided Recurrent Neural Networks
  • 10.5 Automatic Sememe Knowledge Acquisition
  • 10.5.1 Embedding-Based Sememe Prediction
  • 10.5.2 Sememe Prediction with Internal Information
  • 10.5.3 Cross-lingual Sememe Prediction
  • 10.5.4 Connecting HowNet with BabelNet
  • 10.5.5 Summary and Discussion
  • 10.6 Applications
  • 10.6.1 Chinese LIWC Lexicon Expansion
  • 10.6.2 Reverse Dictionary
  • 10.7 Summary and Further Readings
  • References.
  • 11 Legal Knowledge Representation Learning
  • 11.1 Introduction
  • 11.2 Typical Tasks and Real-World Applications
  • 11.3 Legal Knowledge Representation and Acquisition
  • 11.3.1 Legal Textual Knowledge
  • 11.3.2 Legal Structured Knowledge
  • 11.3.3 Discussion
  • 11.4 Knowledge-Guided Legal NLP
  • 11.4.1 Input Augmentation
  • 11.4.2 Architecture Reformulation
  • 11.4.3 Objective Regularization
  • 11.4.4 Parameter Transfer
  • 11.5 Outlook
  • 11.6 Ethical Consideration
  • 11.7 Open Competitions and Benchmarks
  • 11.8 Summary and Further Readings
  • References
  • 12 Biomedical Knowledge Representation Learning
  • 12.1 Introduction
  • 12.1.1 Perspectives for Biomedical NLP
  • 12.1.2 Role of Knowledge in Biomedical NLP
  • 12.2 Biomedical Knowledge Representation and Acquisition
  • 12.2.1 Biomedical Knowledge from Natural Language
  • 12.2.2 Biomedical Knowledge from Biomedical Language Materials
  • 12.3 Knowledge-Guided Biomedical NLP
  • 12.3.1 Input Augmentation
  • 12.3.2 Architecture Reformulation
  • 12.3.3 Objective Regularization
  • 12.3.4 Parameter Transfer
  • 12.4 Typical Applications
  • 12.4.1 Literature Processing
  • 12.4.2 Retrosynthetic Prediction
  • 12.4.3 Diagnosis Assistance
  • 12.5 Advanced Topics
  • 12.6 Summary and Further Readings
  • References
  • 13 OpenBMB: Big Model Systems for Large-Scale Representation Learning
  • 13.1 Introduction
  • 13.2 BMTrain: Efficient Training Toolkit for Big Models
  • 13.2.1 Data Parallelism
  • 13.2.2 ZeRO Optimization
  • 13.2.3 Quickstart of BMTrain
  • 13.3 OpenPrompt and OpenDelta: Efficient Tuning Toolkit for Big Models
  • 13.3.1 Serving Multiple Tasks with a Unified Big Model
  • 13.3.2 Quickstart of OpenPrompt
  • 13.3.3 QuickStart of OpenDelta
  • 13.4 BMCook: Efficient Compression Toolkit for Big Models
  • 13.4.1 Model Quantization
  • 13.4.2 Model Distillation
  • 13.4.3 Model Pruning.
  • 13.4.4 Model MoEfication.

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