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Representation Learning for Natural Language Processing.

Bibliographic Details
Main Author: Liu, Zhiyuan.
Other Authors: Lin, Yankai., Sun, Maosong.
Format: eBook
Language:English
Published: Singapore : Springer Singapore Pte. Limited, 2020.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
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.

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