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Can I read Attention-enabled gated spiking neural P model for aspect-level sentiment classification on EtoBox?
Attention-enabled gated spiking neural P model for aspect-level sentiment classification by Yanping Huang; Hong Peng; Qian Liu; Qian Yang; Jun Wang; David Orellana-Martín; Mario J. Pérez-Jiménez is a Computer Science article available to read on EtoBox.
What is Attention-enabled gated spiking neural P model for aspect-level sentiment classification about?
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.
Who reads Attention-enabled gated spiking neural P model for aspect-level sentiment classification?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Yanping Huang; Hong Peng; Qian Liu; Qian Yang; Jun Wang; David Orellana-Martín; Mario J. Pérez-Jiménez
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)