Representation Learning for Natural Language Processing

2020/01/01 by Zhiyuan Liu, Yankai Lin, Maosong Sun
Computer Science · #Artificial intelligence #Computer science #Data science #Feature learning #Information retrieval #Machine learning #Natural language processing #Representation (politics) #Semantic Web #Semantic Web and Ontologies #Topic Modeling #cs.CL

paper · pdf · doi:10.1007/978-981-15-5573-2

Published in Springer

openalex publication_date 2020/01/01 · arxiv created 2021/02/07 · arxiv updated 2021/02/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for NLP. It also benefit related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology.

Citations

• Zero-Shot Learning by Convex Combination of Semantic Embeddings
• Large-scale Simple Question Answering with Memory Networks
• Deep Convolutional Networks on Graph-Structured Data
• Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
• Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Embedding Entities and Relations for Learning and Inference in Knowledge Bases
• Exploring Models and Data for Image Question Answering
• Efficient Estimation of Word Representations in Vector Space
• Spectral Networks and Locally Connected Networks on Graphs
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Syntactic Topic Models
• Gated Feedback Recurrent Neural Networks
• SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation
• Show and tell: A neural image caption generator
• Traversing Knowledge Graphs in Vector Space
• Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
• A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.
• Algorithms for drawing graphs: an annotated bibliography
• Hearing lips and seeing voices
• Nonlinear Dimensionality Reduction by Locally Linear Embedding
• Matrix Factorization Techniques for Recommender Systems
• Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
• Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures
• A New Approach to Linear Filtering and Prediction Problems
• Gradient-based learning applied to document recognition
• Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
• ImageNet Large Scale Visual Recognition Challenge
• Zero-Shot Learning Through Cross-Modal Transfer
• Exploiting Similarities among Languages for Machine Translation
• Sequence to Sequence Learning with Neural Networks
• Distributed Representations of Sentences and Documents
• Bidirectional recurrent neural networks
• Neural Machine Translation by Jointly Learning to Align and Translate
• Estimation of probabilities from sparse data for the language model component of a speech recognizer
• Gender Differences in Language Use: An Analysis of 14,000 Text Samples
• Collective Classification in Network Data
• DeepWalk
• Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
• Wavelets on graphs via spectral graph theory
• On Smoothing and Inference for Topic Models
• ImageNet classification with deep convolutional neural networks
• Representation Learning: A Review and New Perspectives
• Graph drawing by force‐directed placement
• TransA: An Adaptive Approach for Knowledge Graph Embedding
• Glove: Global Vectors for Word Representation
• Molecular Graph Convolutions: Moving Beyond Fingerprints
• Discriminative Embeddings of Latent Variable Models for Structured Data
• WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation
• A Set of Postulates for the Science of Language
• An Attentive Neural Architecture for Fine-grained Entity Type Classification
• Recurrent Neural Network for Text Classification with Multi-Task Learning
• Complex Embeddings for Simple Link Prediction
• Hierarchical Question-Image Co-Attention for Visual Question Answering
• Diffusion-Convolutional Neural Networks
• A Comprehensive Survey on Cross-modal Retrieval
• Enriching Word Vectors with Subword Information
• Bidirectional Attention Flow for Machine Comprehension
• Language Modeling with Gated Convolutional Networks
• Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion
• Learning Combinatorial Optimization Algorithms over Graphs
• Cross-modal Common Representation Learning by Hybrid Transfer Network
• Graph Convolutional Matrix Completion
• Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
• Learning Deep Generative Models of Graphs
• Neural Relational Inference for Interacting Systems
• Representation Learning on Graphs with Jumping Knowledge Networks
• Distributional Structure
• Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
• Cross-lingual Language Model Pretraining
• Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition
• Unified Language Model Pre-training for Natural Language Understanding and Generation
• Knowledge Graph Representation with Jointly Structural and Textual Encoding
• Image Captioning with Deep Bidirectional LSTMs
• Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
• GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
• From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
• Reducing Transformer Depth on Demand with Structured Dropout
• SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications
• Graph neural networks: A review of methods and applications
• Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
• Learning shape correspondence with anisotropic convolutional neural networks
• Learning to Match Using Local and Distributed Representations of Text for Web Search
• How to Generate a Good Word Embedding?
• Exploiting Generative AI to Scale up Intelligent Tutoring Systems
• Highway Networks