research-article

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

Authors:

Pengtao XieAuthors Info & Claims

ACM Transactions on the Web, Volume 17, Issue 3

Article No.: 20, Pages 1 - 26

https://doi.org/10.1145/3577031

Published: 22 May 2023 Publication History

Abstract

Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems makes it difficult to transfer knowledge from language models pre-trained on general-domain texts. Traditional event detection approaches primarily focus on the general domain and ignore these two problems in the power system domain. To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts. Concretely, the semantic channel refines textual representations with semantic similarity, building the semantic information interaction among potential event-related words. The topological channel generates a relation-type-aware graph modeling word dependencies, and a word-type-aware graph integrating part-of-speech tags. To further reduce errors worsened by professional terminologies in type analysis, a type learning mechanism is designed for updating the representations of both the word type and relation type in the topological channel. In this way, the information sparsity and professional term occurrence problems can be alleviated by enabling interaction between topological and semantic information. Furthermore, to address the lack of labeled data in power systems, we built a Chinese event detection dataset based on electrical Power Event texts, named PoE. In experiments, our model achieves compelling results not only on the PoE dataset, but on general-domain event detection datasets including ACE 2005 and MAVEN.

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Index Terms

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 17, Issue 3

August 2023

302 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3597636

Editor:
Ryen White
Microsoft Research, USA

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Publication History

Published: 22 May 2023

Online AM: 30 January 2023

Accepted: 20 October 2022

Revised: 10 July 2022

Received: 18 January 2022

Published in TWEB Volume 17, Issue 3

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