Relation Classification
141 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
Subtasks
Most implemented papers
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
We present a novel end-to-end neural model to extract entities and relations between them.
Large-scale Exploration of Neural Relation Classification Architectures
Experimental performance on the task of relation classification has generally improved using deep neural network architectures.
ERNIE: Enhanced Language Representation with Informative Entities
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
In this paper, we explore meta-learning for few-shot text classification.
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.
Relation Transformer Network
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
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
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task.
Experiments with Three Approaches to Recognizing Lexical Entailment
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).