Papers with extraction model

6 papers
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web (2020.acl-tutorials)

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Challenge: a tutorial explores the commonalities in the challenges and solutions developed to address information extraction from the World Wide Web.
Approach: This tutorial examines methods for extracting information from the World Wide Web . it explores the commonalities in the challenges and solutions developed to address these different forms of text .
Outcome: This paper examines the commonalities in the challenges and solutions developed to address the World Wide Web.
A Dynamic Self-Evolving Extraction System (2026.acl-demo)

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Challenge: High-quality information extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers.
Approach: They propose a Dynamic Self-Evolving Extraction and Curation Toolkit which continuously improves as it is used to extract structured information from raw text.
Outcome: The proposed toolkit continuously improves as it is used in medical, legal, and HR domains.
Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)

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Challenge: Existing methods to extract event data are laborious to create and limited in size.
Approach: They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles.
Outcome: The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods.
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)

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Challenge: Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data.
Approach: They propose a prompt tuning approach to extract attributes from product information using mixed prompts.
Outcome: The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting.
ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages (2020.acl-main)

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Challenge: Existing work on information extraction from semi-structured websites has relied on manual data annotation and learning a model specific to a given template.
Approach: They propose a solution for “zero-shot” open-domain relation extraction from webpages with previously unseen templates using a graph neural network-based approach.
Outcome: The proposed model provides a 31% gain over baseline for zero-shot extraction in a new subject vertical.
Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025.acl-long)

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Challenge: Recent studies have developed various detection mechanisms to protect against prompt injection attacks.
Approach: They investigate the feasibility of detecting and removing indirect prompt injection attacks . they use two methods to evaluate their performance and train detection models .
Outcome: The proposed method is based on a benchmark dataset and is available on github . it evaluates the performance of existing models and open-source detection models .

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