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Relation Extraction

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하위 태스크: 15
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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

논문

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Relation Extraction

논문

DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations

Multiple Streams of Relation Extraction: Enriching and Recalling in Transformers

Chaining Event Spans for Temporal Relation Grounding

Summarization for Generative Relation Extraction in the Microbiome Domain

Conservative Bias in Large Language Models: Measuring Relation Predictions

Comparative Analysis of AI Agent Architectures for Entity Relationship Classification

CREFT: Sequential Multi-Agent LLM for Character Relation Extraction

Generating Diverse Training Samples for Relation Extraction with Large Language Models

Towards a More Generalized Approach in Open Relation Extraction

VulCPE: Context-Aware Cybersecurity Vulnerability Retrieval and Management

Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting

A Structured Literature Review on Traditional Approaches in Current Natural Language Processing

Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-training

Relation Extraction or Pattern Matching? Unravelling the Generalisation Limits of Language Models for Biographical RE

Relation Extraction Across Entire Books to Reconstruct Community Networks: The AffilKG Datasets

Seeing Beyond the Scene: Enhancing Vision-Language Models with Interactional Reasoning

Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints

Evaluation of LLMs on Long-tail Entity Linking in Historical Documents

Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM

Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction

WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation

Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach

Relation Extraction with Instance-Adapted Predicate Descriptions

Beyond Negation Detection: Comprehensive Assertion Detection Models for Clinical NLP

COMM:Concentrated Margin Maximization for Robust Document-Level Relation Extraction

GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation

Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set

An Empirical Study of Causal Relation Extraction Transfer: Design and Data

Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition

Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence

Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension

Few-shot Continual Relation Extraction via Open Information Extraction

Extraction multi-étiquettes de relations en utilisant des couches de Transformer

MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning

Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction

Beyond Pairwise: Global Zero-shot Temporal Graph Generation