Relation Extraction

476 papers with code • 42 benchmarks • 57 datasets

Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.

Source: Deep Residual Learning for Weakly-Supervised Relation Extraction


Use these libraries to find Relation Extraction models and implementations

Latest papers with no code

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

no code yet • 27 Sep 2022

Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.

A Unified Generative Framework based on Prompt Learning for Various Information Extraction Tasks

no code yet • 23 Sep 2022

In this paper, we propose a novel composable prompt-based generative framework, which could be applied to a wide range of tasks in the field of Information Extraction.

Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes

no code yet • 20 Sep 2022

To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain.

Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction

no code yet • 1 Sep 2022

Our model uses a BERT-based language model to encode local context and a Graph Neural Network (GNN) to represent global document-level syntactic and temporal characteristics.

KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks

no code yet • 1 Sep 2022

As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential.

Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach

no code yet • 1 Sep 2022

2) Balanced Tuning (BT) finetunes the model on the balanced memory data.

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

no code yet • 29 Aug 2022

However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate.

Representing Knowledge by Spans: A Knowledge-Enhanced Model for Information Extraction

no code yet • 20 Aug 2022

To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively.

A Two-Phase Paradigm for Joint Entity-Relation Extraction

no code yet • 18 Aug 2022

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.

A Sequence Tagging based Framework for Few-Shot Relation Extraction

no code yet • 17 Aug 2022

Relation Extraction (RE) refers to extracting the relation triples in the input text.