Pytorch implementation of R-BERT: "Enriching Pre-trained Language Model with Entity Information for Relation Classification"

code of Relation Classification via Multi-Level Attention CNNs

Reinforcement Learning for Relation Classification from Noisy Data(TensorFlow)

A PyTorch-based toolkit for natural language processing

Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

An Evaluation of ChatGPT on Information Extraction task, including Named Entity Recognition (NER), Relation Extraction (RE), Event Extraction (EE) and Aspect-based Sentiment Analysis (ABSA).

PyTorch-IE: State-of-the-art Information Extraction in PyTorch

Pytorch re-implementation of R-BERT model

Tensorflow Implementation of Recurrent Convolutional Neural Network for Relation Extraction

ACL 2019 paper:Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

PyTorch implementation of relation extraction via convolutional neural network with multi-size convolution kernels.

Relation Classificaton based on information enhanced BERT

Convolution Neural Network for classification of semantic relations in a sentence

mul-BERT, the official score on the SemEval 2010 Task 8 dataset is up to 90.72 (Macro-F1).

Few-Shot Relation Extraction with AllenNLP

Large-scale Exploration of Neural Relation Classification Architectures

A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction

Segment graph convolutional neural network for relation classification. Paper in JAMIA.

This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classification using Language Models), a relation classification system that we …

[ACL23] This repository contains the code for our paper "MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset"