Joint Entity and Relation Extraction Edit Task
Natural Language Processing • Information Extraction
About
Scores reported from systems which jointly extract entities and relations.
Benchmarks
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Datasets
Greatest papers with code
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
Span-based Joint Entity and Relation Extraction with Transformer Pre-training
The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass.
Ranked #1 on
Named Entity Recognition
on SciERC
(using extra training data)
JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION RELATION CLASSIFICATION
Adversarial training for multi-context joint entity and relation extraction
EMNLP 2018
• bekou/multihead_joint_entity_relation_extraction
• 
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data.
Ranked #4 on
Relation Extraction
on ACE 2004
Entity, Relation, and Event Extraction with Contextualized Span Representations
IJCNLP 2019
• dwadden/dygiepp
• 
We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric, using extra
training data)
EVENT EXTRACTION JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
ACL 2019
• tsujuifu/pytorch_graph-rel
•
In contrast to previous baselines, we consider the interaction between named entities and relations via a 2nd-phase relation-weighted GCN to better extract relations.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
ACL 2017
• tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
•
Joint extraction of entities and relations is an important task in information extraction.
Ranked #3 on
Relation Extraction
on NYT-single
A Frustratingly Easy Approach for Entity and Relation Extraction
Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model.
Ranked #1 on
Named Entity Recognition
on ACE 2005
JOINT ENTITY AND RELATION EXTRACTION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION STRUCTURED PREDICTION
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
EMNLP 2020
• LorrinWWW/two-are-better-than-one
•
In this work, we propose the novel {\em table-sequence encoders} where two different encoders -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.
Ranked #1 on
Relation Extraction
on CoNLL04
JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION REPRESENTATION LEARNING
A General Framework for Information Extraction using Dynamic Span Graphs
We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs.
Ranked #1 on
Named Entity Recognition
on WLPC
JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
Ranked #4 on
Joint Entity and Relation Extraction
on SciERC
COREFERENCE RESOLUTION JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION