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Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Related Experiment Video

Updated: Oct 2, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

644

A Word-Granular Adversarial Attacks Framework for Causal Event Extraction.

Yu Zhao1,2, Wanli Zuo1,2, Shining Liang1,2

  • 1Colledge of Computer Science and Technology, Jilin University, Changchun 130015, China.

Entropy (Basel, Switzerland)
|February 25, 2022
PubMed
Summary

This study introduces the Actor-Critic Mask Model (ACMM), a novel approach to data augmentation for natural language processing. ACMM adaptively generates masked words to improve model generalization and performance on downstream tasks.

Keywords:
adversarial attackcausal event extractioninformation extractionreinforcement learning

Related Experiment Videos

Last Updated: Oct 2, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

644

Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Augmentation

Background:

  • Masking words is a common data augmentation technique in NLP.
  • Existing rule-based masking methods lack adaptability to specific downstream tasks.

Purpose of the Study:

  • To propose a novel, adaptive masking word generator named Actor-Critic Mask Model (ACMM).
  • To enhance model generalization and performance in NLP tasks through adaptive data augmentation.

Main Methods:

  • Developed the Actor-Critic Mask Model (ACMM) for adaptive masking.
  • Evaluated ACMM on two causal event extraction datasets.

Main Results:

  • ACMM significantly enhances model generalization compared to rule-based methods.
  • The proposed method improves overall model performance on causal event extraction tasks.

Conclusions:

  • The Actor-Critic Mask Model (ACMM) offers an effective adaptive strategy for data augmentation in NLP.
  • Adaptive masking improves model performance and generalization in downstream tasks like event extraction.