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Web"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            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\n            <jats:bold>Multi-Channel graph neural network utilizing Type information for Event Detection<\/jats:bold>\n            in power systems, named\n            <jats:bold>MC-TED<\/jats:bold>\n            , 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\n            <jats:bold>PoE<\/jats:bold>\n            . 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.\n          <\/jats:p>","DOI":"10.1145\/3577031","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T11:56:46Z","timestamp":1675079806000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1612-4644","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-0055","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6818-7439","authenticated-orcid":false,"given":"Lihong","family":"Wang","sequence":"additional","affiliation":[{"name":"National Computer Network Emergency Response Technical Team\/Coordination Center of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2513-3822","authenticated-orcid":false,"given":"Cheng","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0794-9932","authenticated-orcid":false,"given":"Yiming","family":"Hei","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4865-982X","authenticated-orcid":false,"given":"Jiawei","family":"Sheng","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1930-3848","authenticated-orcid":false,"given":"Qingyun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9123-5133","authenticated-orcid":false,"given":"Shan","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0521-174X","authenticated-orcid":false,"given":"Pengtao","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, UC San Diego, United States"}]}],"member":"320","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"J. 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IEEE, 1\u20136."},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783307"},{"key":"e_1_3_2_54_2","volume-title":"8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020","author":"Vashishth Shikhar","year":"2020","unstructured":"Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar. 2020. Composition-based multi-relational graph convolutional networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020. https:\/\/openreview.net\/forum?id=BylA_C4tPr."},{"key":"e_1_3_2_55_2","volume-title":"6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. 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