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Scores reported from systems which jointly extract entities and relations.

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Datasets

Greatest papers with code

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

27 Oct 2016shanzhenren/CoType

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.

JOINT ENTITY AND RELATION EXTRACTION TEXT SEGMENTATION

Span-based Joint Entity and Relation Extraction with Transformer Pre-training

17 Sep 2019markus-eberts/spert

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.

JOINT ENTITY AND RELATION EXTRACTION

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 ENTITY AND RELATION EXTRACTION

A Frustratingly Easy Approach for Entity and Relation Extraction

24 Oct 2020princeton-nlp/PURE

Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model.

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.

JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION REPRESENTATION LEARNING

A General Framework for Information Extraction using Dynamic Span Graphs

NAACL 2019 luanyi/DyGIE

We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs.

JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION