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arXiv · 2601.06637

Efficient Aspect Term Extraction using Spiking Neural Network

Abhishek Kumar Mishra·Arya Somasundaram·Anup Das·Nagarajan Kandasamy
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Abstract

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.

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Abhishek Kumar Mishra, Arya Somasundaram, Anup Das, Nagarajan Kandasamy. 2026-01-10. Efficient Aspect Term Extraction using Spiking Neural Network. https://arxiv.org/abs/2601.06637

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