Joint Entity and Relation Extraction

37 papers with code • 6 benchmarks • 6 datasets

Scores reported from systems which jointly extract entities and relations.

Most implemented papers

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

luanyi/DyGIE EMNLP 2018

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.

A General Framework for Information Extraction using Dynamic Span Graphs

luanyi/DyGIE NAACL 2019

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

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

shanzhenren/CoType 27 Oct 2016

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.

Entity, Relation, and Event Extraction with Contextualized Span Representations

dwadden/dygiepp IJCNLP 2019

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.

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

markus-eberts/spert 17 Sep 2019

The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass.

Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders

LorrinWWW/two-are-better-than-one EMNLP 2020

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.

A Frustratingly Easy Approach for Entity and Relation Extraction

princeton-nlp/PURE NAACL 2021

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

Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction

pgcool/TF-MTRNN COLING 2016

This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.

Adversarial training for multi-context joint entity and relation extraction

bekou/multihead_joint_entity_relation_extraction EMNLP 2018

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.