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

11/15/2022
by   Qian Li, et al.
Internet Society of China
CSIRO
iie.ac.cn
Beihang University
University of California, San Diego
0

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.

READ FULL TEXT

page 6

page 21

12/03/2020

Label Enhanced Event Detection with Heterogeneous Graph Attention Networks

Event Detection (ED) aims to recognize instances of specified types of e...
02/25/2020

Event Detection with Relation-Aware Graph Convolutional Neural Networks

Event detection (ED), a key subtask of information extraction, aims to r...
12/15/2022

RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech Synthesis

With the advent of deep learning, a huge number of text-to-speech (TTS) ...
07/02/2016

Representation learning for very short texts using weighted word embedding aggregation

Short text messages such as tweets are very noisy and sparse in their us...
05/01/2018

Nugget Proposal Networks for Chinese Event Detection

Neural network based models commonly regard event detection as a word-wi...
07/02/2020

Improving Event Detection using Contextual Word and Sentence Embeddings

The task of Event Detection (ED) is a subfield of Information Extraction...

Please sign up or login with your details

Forgot password? Click here to reset