  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.
