Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification
A Dependency-Based Neural Network for Relation Classification
Learning Multi-way Relations via Tensor Decomposition with Neural Networks
Guided Learning for Bidirectional Sequence Classification
RelNet: End-to-end Modeling of Entities&Relations
The Latent Relation Mapping Engine: Algorithm and Experiments
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network
Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification
A Review of Relation Classification with Convolutional Neural Network
Multi-instance Multi-label Learning for Relation Extraction
Conditional High-Order Boltzmann Machines for Supervised Relation Learning
Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models
Distant Supervision for Relation Extraction with Hierarchical Attention and Entity Descriptions
Supervised Neural Models Revitalize the Open Relation Extraction
Recurrent networks with attention and convolutional networks for sentence representation and classification
Context-Aware End-To-End Relation Extracting from Clinical Texts with Attention-based Bi-Tree-GRU
Combining Neural Networks and Log-linear Models to Improve Relation Extraction
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Sentiment classification using Comprehensive Attention Recurrent models
Multichannel Convolutional Neural Network for Biological Relation Extraction
WSABIE: Scaling Up to Large Vocabulary Image Annotation
Smoke rings from smoke
A noninvasive blood gluco se measurement system by arduino and near-infrared
Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization