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
Benchmarks
These leaderboards are used to track progress in Relation Classification
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TACRED
FewRel
FewRel 2.0
Discovery
GUM
LabPics
Subtasks
Latest papers
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning.
Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information
bnlnlp/ppi-relation-extraction
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On the other hand, machine learning methods to automate PPI knowledge extraction from the scientific literature have been limited by a shortage of appropriate annotated data.
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
Recent zero-shot classification methods converted the task to other NLP tasks (e. g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data.
Graph Language Models
In our work we introduce a novel LM type, the Graph Language Model (GLM), that integrates the strengths of both approaches and mitigates their weaknesses.
CreoleVal: Multilingual Multitask Benchmarks for Creoles
hclent/creoleval
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Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
pnborchert/core
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To evaluate the performance of state-of-the-art RC models on the CORE dataset, we conduct experiments in the few-shot domain adaptation setting.
Rationale-Enhanced Language Models are Better Continual Relation Learners
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations.
Multiple Relations Classification using Imbalanced Predictions Adaptation
sa5r/mrca
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To the best of our knowledge, this work is the first that recognizes the imbalanced predictions within the relation classification task.
Synthesizing Political Zero-Shot Relation Classification via Codebook Knowledge, NLI, and ChatGPT
Our study underscores the efficacy of leveraging transfer learning and existing expertise to enhance research efficiency and scalability in this area.
Explaining Relation Classification Models with Semantic Extents
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task.
