Relation Classification

140 papers with code • 8 benchmarks • 23 datasets

Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.

Source: Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text

Most implemented papers

Large-scale Exploration of Neural Relation Classification Architectures

aidantee/MASS EMNLP 2018

Experimental performance on the task of relation classification has generally improved using deep neural network architectures.

ERNIE: Enhanced Language Representation with Informative Entities

thunlp/ERNIE ACL 2019

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.

Few-shot Text Classification with Distributional Signatures

YujiaBao/Distributional-Signatures ICLR 2020

In this paper, we explore meta-learning for few-shot text classification.

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.

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

microsoft/K-Adapter Findings (ACL) 2021

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.

Relation Transformer Network

rajatkoner08/rtn 13 Apr 2020

In this work, we propose a novel transformer formulation for scene graph generation and relation prediction.

RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction

declare-lab/relationprompt Findings (ACL) 2022

We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.

Explaining Relation Classification Models with Semantic Extents

mslars/semantic_extents 4 Aug 2023

We introduce semantic extents, a concept to analyze decision patterns for the relation classification task.

Experiments with Three Approaches to Recognizing Lexical Entailment

context-mover/HypEval 31 Jan 2014

Two general strategies for RLE have been proposed: One strategy is to manually construct an asymmetric similarity measure for context vectors (directional similarity) and another is to treat RLE as a problem of learning to recognize semantic relations using supervised machine learning techniques (relation classification).