Enhancing pre-trained language model by answering natural questions for event extraction

Our results show that the use of topic words and question answering techniques can effectively address the challenges faced by event extraction and pave the way for the development of more accurate and robust event extraction systems.

Authors

Qing Han (ORCID: https://orcid.org/0000-0002-4958-1315)
Yuxin Zhang (ORCID: https://orcid.org/0009-0000-6729-0004)

Institutions

Zhejiang Chinese Medical University (CN)

Publication Details

Journal: Frontiers in Artificial Intelligence
Published: 2025-04-24
DOI: https://doi.org/10.3389/frai.2025.1520290
Citations: 1
Primary Topic: Topic Modeling
Type: article
Field-Weighted Citation Impact: 2.48

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article

Enhancing pre-trained language model by answering natural questions for event extraction

Qing Han, Yuxin Zhang

Apr 24, 2025

1 citations

Frontiers in Artificial Intelligence

Topic Modeling

2.48

article

Enhancing pre-trained language model by answering natural questions for event extraction

Qing Han, Yuxin Zhang

April 24, 2025article en

1 citations

Abstract

Frontiers in Artificial IntelligenceVol. 8

Zhejiang Chinese Medical University (CN)

Openalex Percentile: Top 11%

Topic Modeling

2.48

DOI Open Access PDF OpenAlex

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Enhancing pre-trained language model by answering natural questions for event extraction — Qing Han, Yuxin Zhang · Frontiers in Artificial Intelligence (2025) | TGRS Research Map | TGRS