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Related Experiment Video

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Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
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Advancing document-level event extraction: Integration across texts and reciprocal feedback.

Min Zuo1,2, Jiaqi Li1,2, Di Wu3

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

Mathematical Biosciences and Engineering : MBE
|December 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Integration Across Texts and Reciprocal Feedback (IATRF) model for document-level event extraction. IATRF enhances context understanding across sentences, improving information retrieval from lengthy texts.

Keywords:
Graph Convolutional Networksdocument-level event extractionentity extractionheterogeneous graphstransformer

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Artificial Intelligence

Background:

  • Document-level event extraction aims to identify events in long texts.
  • Existing methods struggle to integrate cross-sentence contextual information effectively.
  • Challenges include representing long-distance document context and dispersed event arguments.

Purpose of the Study:

  • To propose a novel document-level event extraction model, Integration Across Texts and Reciprocal Feedback (IATRF).
  • To improve the incorporation of contextual information spanning across sentences.
  • To enhance the recognition of event arguments, especially when dispersed across sentences.

Main Methods:

  • Constructing a heterogeneous graph to link document and entity information.
  • Employing a graph convolutional network (GCN) for context-aware semantic information acquisition.
  • Utilizing a Transformer classifier for multi-label event type classification.
  • Introducing a Reciprocal Feedback Argument Extraction strategy for argument recognition.

Main Results:

  • The IATRF model demonstrated superior performance over existing methods on the COSM and ChFinAnn datasets.
  • The model achieved a higher F1 value, confirming its effectiveness.
  • Successfully addressed challenges in long-distance context representation and cross-sentence argument dispersion.

Conclusions:

  • The proposed IATRF model significantly advances document-level event extraction.
  • IATRF effectively captures document-level context and handles dispersed event arguments.
  • This approach offers a robust solution for complex information extraction tasks from extensive texts.