SOTAVerified

Relation Extraction

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

Papers

Showing 110 of 1977 papers

TitleStatusHype
DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations0
Multiple Streams of Relation Extraction: Enriching and Recalling in Transformers0
Chaining Event Spans for Temporal Relation GroundingCode0
Summarization for Generative Relation Extraction in the Microbiome Domain0
Conservative Bias in Large Language Models: Measuring Relation Predictions0
Comparative Analysis of AI Agent Architectures for Entity Relationship ClassificationCode0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction0
Generating Diverse Training Samples for Relation Extraction with Large Language Models0
Towards a More Generalized Approach in Open Relation ExtractionCode0
Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive PromptingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DREEAMF167.53Unverified
2KD-Rb-lF167.28Unverified
3SSAN-RoBERTa-large+AdaptationF165.92Unverified
4SAIS-RoBERTa-largeF165.11Unverified
5Eider-RoBERTa-largeF164.79Unverified
6DocuNet-RoBERTa-largeF164.55Unverified
7CGM2IR-RoBERTalargeF163.89Unverified
8SETE-Roberta-largeF163.74Unverified
9ATLOP-RoBERTa-largeF163.4Unverified
10DRE-MIR-BERTbaseF163.15Unverified