Files and links (1)
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Efficient Aspect Term Extraction using Spiking Neural Network
ArXiv.org
10 Jan 2026
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.
Metrics
3 Record Views
Details
- Title
- Efficient Aspect Term Extraction using Spiking Neural Network
- Creators
- Abhishek Kumar Mishra - Drexel UniversityArya Somasundaram - University of California, Los AngelesAnup Das - Drexel UniversityNagarajan Kandasamy - Drexel University
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Other Identifier
- 991022154869504721