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New CalOn model unifies entity, relation, and event extraction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CalOn, a 0.6B-parameter encoder designed for unified entity, relation, and event extraction across various datasets and schemas. This model jointly processes text and type definitions to identify spans, relations, and event triggers/arguments, leveraging combined evidence for more accurate typing. Trained on diverse sources and incorporating a reward mechanism for correct extractions, CalOn demonstrates superior performance across 40 benchmarks, outperforming existing baselines, particularly in relation extraction and event extraction tasks. AI

IMPACT This model could improve the accuracy and transferability of information extraction tasks across diverse datasets and schemas.

RANK_REASON The item is a research paper detailing a new model for information extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CalOn model unifies entity, relation, and event extraction

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The item is a research paper detailing a new model for information extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yang Wang, Hanlin Xiao, Yanan Ma, Chenghao Xiao, Chenghua Lin ·

    Calibrated Typed Decisions for Entity, Relation, and Event Extraction

    arXiv:2610.17113v1 Announce Type: cross Abstract: Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remains challenging. Supporting user-specified types alone does not resolve this probl…

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