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Movie Scene Event Extraction with Graph Attention Network Based on Argument Correlation Information.

Qian Yi^1,2, Guixuan Zhang¹, Jie Liu¹

¹Beijing Engineering Research Center of Digital Content Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100038, China.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This summary is machine-generated.

This study introduces a novel method for movie scene event extraction by modeling correlations between argument roles using a superior role concept (SRC). This approach enhances both trigger and argument extraction, improving overall performance in media analysis.

Keywords:

argument correlation information event extraction graph attention network

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Area of Science:

Natural Language Processing
Media Analysis
Artificial Intelligence

Background:

Movie scene event extraction is crucial for media analysis but underexplored.
Existing methods struggle with argument roles sharing similar characteristics in movie scripts.

Purpose of the Study:

To propose a new model for movie scene event extraction that leverages correlations between argument roles.
To improve trigger and argument identification and classification in movie scripts.

Main Methods:

Introduced the superior role concept (SRC) to model correlations between argument roles.
Developed an attentive high-level argument role module to capture SRC information.
Utilized an SRC-based graph attention network (GAT) to integrate role correlation into semantic embeddings.

Main Results:

The proposed model achieved superior performance compared to existing methods on a new dataset (MovieSceneEvent) and a benchmark dataset.
Demonstrated that incorporating argument role correlation information significantly enhances movie scene event extraction.

Conclusions:

The novel approach effectively models argument role correlations, leading to improved movie scene event extraction.
The SRC concept and GAT integration offer a promising direction for advancing media analysis tasks.