Joint Entity and Relation Extraction

51 papers with code • 15 benchmarks • 12 datasets

Joint Entity and Relation Extraction is the task of extracting entity mentions and semantic relations between entities from unstructured text with a single model.

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.

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.

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.

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.

A sequence-to-sequence approach for document-level relation extraction

johngiorgi/seq2rel BioNLP (ACL) 2022

In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components.

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.