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

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Showing 1–10 of 1977 papers

TitleStatusHype
DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations—0
Multiple Streams of Relation Extraction: Enriching and Recalling in Transformers—0
Chaining Event Spans for Temporal Relation GroundingCode0
Summarization for Generative Relation Extraction in the Microbiome Domain—0
Conservative Bias in Large Language Models: Measuring Relation Predictions—0
Comparative Analysis of AI Agent Architectures for Entity Relationship ClassificationCode0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction—0
Generating Diverse Training Samples for Relation Extraction with Large Language Models—0
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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All datasetsDocREDTACREDSemEval-2010 Task-8ACE 2005CoNLL04Adverse Drug Events (ADE) CorpusWebNLGChemProtACE 2004NYT11-HRLCDRGDA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPF191.9—Unverified
2RIFREF191.3—Unverified
3REDNF191—Unverified
4SPOTF190.6—Unverified
5KLGF190.5—Unverified
6RELAF190.4—Unverified
7Skeleton-Aware BERTF190.36—Unverified
8LUKEF190.3—Unverified
9KnowPromptF190.3—Unverified
10EPGNNF190.2—Unverified