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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>The text classification of power equipment defect is of great significance to equipment health condition evaluation and power equipment maintenance decisions. Most of the existing classification methods do not sufficiently consider the semantic relation between words in the same sentence and cannot extract deep semantic features. To tackle those problems, this article proposes a novel classification method by combining the self-attention mechanism and multi-channel pyramid convolution neural networks. We utilize the bidirectional gated recurrent unit to model the text sequence and, on this basis, improve self-attention layer to dot multiplication on the forward and backward features to obtain the global attention score. Thereby, effective features are enhanced, invalid features are weakened, and important text representation vectors are obtained. To solve the problem that the shallow network structure cannot extract deep semantic features, we design a multi-channel pyramid convolution network, which first extracts deep text features from the channels of different windows and then fuses the text features of each channel. By comparing with the state-of-the-art methods, the model in this article has better performance in text classification of power equipment defects.<\/jats:p>","DOI":"10.1145\/3464380","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T18:59:58Z","timestamp":1628794798000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Classification Model SA-MPCNN for Power Equipment Defect Text"],"prefix":"10.1145","volume":"20","author":[{"given":"Xiuxia","family":"Tian","sequence":"first","affiliation":[{"name":"ShangHai University of Electric Power, Shanghai, China"}]},{"given":"Can","family":"Li","sequence":"additional","affiliation":[{"name":"ShangHai University of Electric Power, Shanghai, China"}]},{"given":"Bo","family":"Zhao","sequence":"additional","affiliation":[{"name":"ShangHai University of Electric Power, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. 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