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• W4308901712 abstract "Gated spiking neural P (GSNP) model is a recently developed recurrent-like network, which is abstracted by nonlinear spiking mechanism of nonlinear spiking neural P systems. In this study, a modification of GSNP is combined with attention mechanism to develop a novel model for sentiment classification, called attention-enabled GSNP model or termed as AGSNP model. The AGSNP model has two channels that process content words and aspect item respectively, where two modified GSNPs are used to obtain dependencies between content words and between aspect words. Moreover, two attention components are used to establish semantic correlation between content words and aspect item. Comparative experiments on three real data sets and several baseline models are conducted to verify the effectiveness of the AGSNP model. The comparison results demonstrate that the AGSNP model is competent for aspect-level sentiment classification tasks." @default.
• W4308901712 created "2022-11-19" @default.
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• W4308901712 date "2023-01-01" @default.
• W4308901712 modified "2023-10-18" @default.
• W4308901712 title "Attention-enabled gated spiking neural P model for aspect-level sentiment classification" @default.
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• W4308901712 doi "https://doi.org/10.1016/j.neunet.2022.11.006" @default.
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