Information extraction dataset zoo.

Other Datasets

• WikiReading

FewRel : A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

Matching the Blanks : Distributional Similarity for Relation Learning ACL2019

Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification ACL2019

Position-aware attention and supervised data improve slot filling.

Matching the Blanks : Distributional Similarity for Relation Learning ACL2019

Matching the Blanks : Distributional Similarity for Relation Learning

Context-Aware Representations for Knowledge Base Relation Extraction

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks

Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction EMNLP2018

Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention EMNLP2018

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks NAACL2019

Incorporating Relation Paths in Neural Relation Extraction EMNLP2017

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

Joint Extraction of Entities and Relations with a Hierarchical Multi-task Tagging Model

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

Joint Extraction of Entities and Relations with a Hierarchical Multi-task Tagging Model

Indirect Supervision for Relation Extraction Using Question-Answer Pairs

CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases.

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism

Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Relation Adversarial Network for Low Resource Knowledge Graph Completion

CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases.

Indirect Supervision for Relation Extraction Using Question-Answer Pairs

CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases.

Extracting relational facts by an end-to-end neural model with copy mechanism

MrMep: joint extraction of multiple relations and multiple entity pairs based on triplet attention

Matching the Blanks : Distributional Similarity for Relation Learning ACL2019

Relation classification via recurrent neural network

T-Rex : A Large Scale Alignment of Natural Language with Knowledge Base Triples

K-ADAPTER: Infusing Knowledge into Pre-Trained Models with Adapters

too many papers

Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection

Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction