Efficient Aspect Term Extraction using Spiking Neural Network
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914245435719680 |
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| author | Mishra, Abhishek Kumar Somasundaram, Arya Das, Anup Kandasamy, Nagarajan |
| author_facet | Mishra, Abhishek Kumar Somasundaram, Arya Das, Anup Kandasamy, Nagarajan |
| contents | Aspect Term Extraction (ATE) identifies aspect terms in review sentences, a key subtask of sentiment analysis. While most existing approaches use energy-intensive deep neural networks (DNNs) for ATE as sequence labeling, this paper proposes a more energy-efficient alternative using Spiking Neural Networks (SNNs). Using sparse activations and event-driven inferences, SNNs capture temporal dependencies between words, making them suitable for ATE. The proposed architecture, SpikeATE, employs ternary spiking neurons and direct spike training fine-tuned with pseudo-gradients. Evaluated on four benchmark SemEval datasets, SpikeATE achieves performance comparable to state-of-the-art DNNs with significantly lower energy consumption. This highlights the use of SNNs as a practical and sustainable choice for ATE tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06637 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Aspect Term Extraction using Spiking Neural Network Mishra, Abhishek Kumar Somasundaram, Arya Das, Anup Kandasamy, Nagarajan Computation and Language Aspect Term Extraction (ATE) identifies aspect terms in review sentences, a key subtask of sentiment analysis. While most existing approaches use energy-intensive deep neural networks (DNNs) for ATE as sequence labeling, this paper proposes a more energy-efficient alternative using Spiking Neural Networks (SNNs). Using sparse activations and event-driven inferences, SNNs capture temporal dependencies between words, making them suitable for ATE. The proposed architecture, SpikeATE, employs ternary spiking neurons and direct spike training fine-tuned with pseudo-gradients. Evaluated on four benchmark SemEval datasets, SpikeATE achieves performance comparable to state-of-the-art DNNs with significantly lower energy consumption. This highlights the use of SNNs as a practical and sustainable choice for ATE tasks. |
| title | Efficient Aspect Term Extraction using Spiking Neural Network |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.06637 |