Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents

Author:

Zhang Qingchuan12,Chen Hongxi12,Cai Yuanyuan12ORCID,Dong Wei12ORCID,Liu Peng3

Affiliation:

1. National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China

2. China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing 100048, China

3. Department of Agricultural Food Standardization Institute, China National Institute of Standardization, Beijing 100191, China

Abstract

The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according to the importance of argument roles. To address these issues, we propose a model based on graph neural networks which realizes the semantic interaction among documents, sentences, and entities. Additionally, our model adopts a dynamic argument detection strategy, extracting arguments depending on their number in correspondence with each role. The experimental results confirm the outperformance of our model, which surpasses previous methods by 7% and 1.9% in terms of an F1 score.

Funder

Project of Cultivation for Young Top-notch Talents of Beijing Municipal Institutions

R&D Program of Beijing Municipal Commission of Education

Humanity and Social Science Youth Foundation of Ministry of Education of China

Innovation Research Special Project of the IFLYTEK for University Intelligent Teaching

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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