Abstract
The GSNP model is a new recurrent-like network inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems. In this study, a novel sentiment classification model MA-BiGSNP is established by using bidirectional GSNP model combined with multi-attention mechanism. BiGSNP, which is created by two GSNP models with opposite directions, is used to capture semantic correlations between word contexts in a sentence. The multi-attention mechanism simulates the variety of relationships between sentences as well as the significance of words in sentences. To evaluate the effectiveness of the proposed MA-BiGSNP model, we perform comparative experiments and ablation experiments on five real datasets and twelve baseline models. The experimental results show that the proposed MA-BiGSNP model is effective for sentiment classification task.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
