Papers with information extraction pipelines

2 papers
GLiREL - Generalist Model for Zero-Shot Relation Extraction (2025.naacl-long)

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Challenge: Existing approaches to zero-shot named entity recognition rely on distant supervision and training data for unseen labels.
Approach: They propose an efficient architecture and training paradigm for zero-shot relation classification . they use a protocol to generate multiple relation labels in a single forward pass .
Outcome: The proposed architecture and training paradigm achieve state-of-the-art results on the zero-shot relation classification task.
LLM4RE: A Data-centric Feasibility Study for Relation Extraction (2025.coling-main)

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Challenge: Relation Extraction (RE) is a critical step in information extraction due to its wide-scale applicability for downstream applications such as Knowledge Base creation and Question Answering (QA).
Approach: They propose to conduct the first feasibility analysis to explore the viability of Large Language Models for RE by investigating their robustness to various RE scenarios stemming from data-specific characteristics.
Outcome: The proposed models are robust to various RE scenarios stemming from data-specific characteristics, but their performance is not yet fully understood.

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