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TGIN: Document-level event extraction with two-phase graph inference network.

Yu Zhong¹, Bo Shen¹, Tao Wang¹

¹School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 3, 2024

Summary

This summary is machine-generated.

This study introduces a Two-phase Graph Inference Network (TGIN) for document-level event extraction. TGIN enhances precision by explicitly modeling key information and reducing noise from irrelevant entities.

Keywords:

Attention mechanism Document-level event extraction Graph inference network

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Area of Science:

Natural Language Processing
Information Extraction
Artificial Intelligence

Background:

Document-level event extraction is challenging due to scattered entities across multiple sentences.
Existing methods struggle with implicit information modeling and irrelevant entity consideration, leading to noise and reduced efficiency.
Effective modeling of entity interactions is crucial for accurate document-level event extraction.

Purpose of the Study:

To propose a novel Two-phase Graph Inference Network (TGIN) for improved document-level event extraction.
To address limitations of previous methods by explicitly modeling key information and reducing irrelevant entity influence.
To enhance the efficiency and precision of extracting event records from entire documents.

Main Methods:

Constructing a heterogeneous document-level graph in the first phase to capture complex interactions and acquire document-aware features.
Employing a key information aggregator with an attention mechanism to explicitly aggregate key sentences for entity pairs.
Building an entity-level graph in the second phase using predicted entity links as prior information to model interactions between related entity pairs.

Main Results:

The proposed TGIN framework demonstrates superior performance on the ChFinAnn dataset for document-level event extraction.
Explicitly aggregating key information and focusing on relevant entity pairs significantly improved extraction precision.
The two-phase graph inference approach effectively reduced error propagation and noise in event extraction.

Conclusions:

The TGIN approach offers a significant advancement in document-level event extraction by effectively addressing noise and efficiency issues.
Explicitly modeling key information and entity interactions through a two-phase graph inference network leads to superior extraction performance.
This framework provides a robust solution for extracting complex event records from unstructured documents.