Representation Learning for Natural Language Processing.
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| Place / Publishing House: | Singapore : : Springer Singapore Pte. Limited,, 2020. ©2020. |
| Year of Publication: | 2020 |
| Edition: | 1st ed. |
| Language: | English |
| Online Access: | |
| Physical Description: | 1 online resource (319 pages) |
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Table of Contents:
- Intro
- Preface
- Acknowledgements
- Contents
- Acronyms
- Symbols and Notations
- 1 Representation Learning and NLP
- 1.1 Motivation
- 1.2 Why Representation Learning Is Important for NLP
- 1.3 Basic Ideas of Representation Learning
- 1.4 Development of Representation Learning for NLP
- 1.5 Learning Approaches to Representation Learning for NLP
- 1.6 Applications of Representation Learning for NLP
- 1.7 The Organization of This Book
- References
- 2 Word Representation
- 2.1 Introduction
- 2.2 One-Hot Word Representation
- 2.3 Distributed Word Representation
- 2.3.1 Brown Cluster
- 2.3.2 Latent Semantic Analysis
- 2.3.3 Word2vec
- 2.3.4 GloVe
- 2.4 Contextualized Word Representation
- 2.5 Extensions
- 2.5.1 Word Representation Theories
- 2.5.2 Multi-prototype Word Representation
- 2.5.3 Multisource Word Representation
- 2.5.4 Multilingual Word Representation
- 2.5.5 Task-Specific Word Representation
- 2.5.6 Time-Specific Word Representation
- 2.6 Evaluation
- 2.6.1 Word Similarity/Relatedness
- 2.6.2 Word Analogy
- 2.7 Summary
- References
- 3 Compositional Semantics
- 3.1 Introduction
- 3.2 Semantic Space
- 3.2.1 Vector Space
- 3.2.2 Matrix-Vector Space
- 3.3 Binary Composition
- 3.3.1 Additive Model
- 3.3.2 Multiplicative Model
- 3.4 N-Ary Composition
- 3.4.1 Recurrent Neural Network
- 3.4.2 Recursive Neural Network
- 3.4.3 Convolutional Neural Network
- 3.5 Summary
- References
- 4 Sentence Representation
- 4.1 Introduction
- 4.2 One-Hot Sentence Representation
- 4.3 Probabilistic Language Model
- 4.4 Neural Language Model
- 4.4.1 Feedforward Neural Network Language Model
- 4.4.2 Convolutional Neural Network Language Model
- 4.4.3 Recurrent Neural Network Language Model
- 4.4.4 Transformer Language Model
- 4.4.5 Extensions
- 4.5 Applications
- 4.5.1 Text Classification.
- 4.5.2 Relation Extraction
- 4.6 Summary
- References
- 5 RETRACTED CHAPTER: Document Representation
- 6 Sememe Knowledge Representation
- 6.1 Introduction
- 6.1.1 Linguistic Knowledge Graphs
- 6.2 Sememe Knowledge Representation
- 6.2.1 Simple Sememe Aggregation Model
- 6.2.2 Sememe Attention over Context Model
- 6.2.3 Sememe Attention over Target Model
- 6.3 Applications
- 6.3.1 Sememe-Guided Word Representation
- 6.3.2 Sememe-Guided Semantic Compositionality Modeling
- 6.3.3 Sememe-Guided Language Modeling
- 6.3.4 Sememe Prediction
- 6.3.5 Other Sememe-Guided Applications
- 6.4 Summary
- References
- 7 World Knowledge Representation
- 7.1 Introduction
- 7.1.1 World Knowledge Graphs
- 7.2 Knowledge Graph Representation
- 7.2.1 Notations
- 7.2.2 TransE
- 7.2.3 Extensions of TransE
- 7.2.4 Other Models
- 7.3 Multisource Knowledge Graph Representation
- 7.3.1 Knowledge Graph Representation with Texts
- 7.3.2 Knowledge Graph Representation with Types
- 7.3.3 Knowledge Graph Representation with Images
- 7.3.4 Knowledge Graph Representation with Logic Rules
- 7.4 Applications
- 7.4.1 Knowledge Graph Completion
- 7.4.2 Knowledge-Guided Entity Typing
- 7.4.3 Knowledge-Guided Information Retrieval
- 7.4.4 Knowledge-Guided Language Models
- 7.4.5 Other Knowledge-Guided Applications
- 7.5 Summary
- References
- 8 Network Representation
- 8.1 Introduction
- 8.2 Network Representation
- 8.2.1 Spectral Clustering Based Methods
- 8.2.2 DeepWalk
- 8.2.3 Matrix Factorization Based Methods
- 8.2.4 Structural Deep Network Methods
- 8.2.5 Extensions
- 8.2.6 Applications
- 8.3 Graph Neural Networks
- 8.3.1 Motivations
- 8.3.2 Graph Convolutional Networks
- 8.3.3 Graph Attention Networks
- 8.3.4 Graph Recurrent Networks
- 8.3.5 Extensions
- 8.3.6 Applications
- 8.4 Summary
- References.
- 9 Cross-Modal Representation
- 9.1 Introduction
- 9.2 Cross-Modal Representation
- 9.2.1 Visual Word2vec
- 9.2.2 Cross-Modal Representation for Zero-Shot Recognition
- 9.2.3 Cross-Modal Representation for Cross-Media Retrieval
- 9.3 Image Captioning
- 9.3.1 Retrieval Models for Image Captioning
- 9.3.2 Generation Models for Image Captioning
- 9.3.3 Neural Models for Image Captioning
- 9.4 Visual Relationship Detection
- 9.4.1 Visual Relationship Detection with Language Priors
- 9.4.2 Visual Translation Embedding Network
- 9.4.3 Scene Graph Generation
- 9.5 Visual Question Answering
- 9.5.1 VQA and VQA Datasets
- 9.5.2 VQA Models
- 9.6 Summary
- References
- 10 Resources
- 10.1 Open-Source Frameworks for Deep Learning
- 10.1.1 Caffe
- 10.1.2 Theano
- 10.1.3 TensorFlow
- 10.1.4 Torch
- 10.1.5 PyTorch
- 10.1.6 Keras
- 10.1.7 MXNet
- 10.2 Open Resources for Word Representation
- 10.2.1 Word2Vec
- 10.2.2 GloVe
- 10.3 Open Resources for Knowledge Graph Representation
- 10.3.1 OpenKE
- 10.3.2 Scikit-Kge
- 10.4 Open Resources for Network Representation
- 10.4.1 OpenNE
- 10.4.2 GEM
- 10.4.3 GraphVite
- 10.4.4 CogDL
- 10.5 Open Resources for Relation Extraction
- 10.5.1 OpenNRE
- References
- 11 Outlook
- 11.1 Introduction
- 11.2 Using More Unsupervised Data
- 11.3 Utilizing Fewer Labeled Data
- 11.4 Employing Deeper Neural Architectures
- 11.5 Improving Model Interpretability
- 11.6 Fusing the Advances from Other Areas
- References
- Correction to: Z. Liu et al., Representation Learning for Natural Language Processing, https://doi.org/10.1007/978-981-15-5573-2.