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Efficient Aspect Term Extraction using Spiking Neural Network

arXiv 2601.06637 ↗First posted 10 Jan 2026cs.CLcited by 0

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Abstract (arXiv)

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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References · 29

  1. 2023GPT-4 Technical Reportcited by 18,396
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  3. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
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  11. 2020DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networkscited by 71
  12. 2021A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysiscited by 62
17 more
  1. 2018A Unified Model for Opinion Target Extraction and Target Sentiment Predictioncited by 56
  2. 2017A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)cited by 52
  3. 2018Double Embeddings and CNN-based Sequence Labeling for Aspect Extractioncited by 42
  4. 2016Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardwarecited by 40
  5. 2018Aspect Term Extraction with History Attention and Selective Transformationcited by 37
  6. 2016Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysiscited by 35
  7. 2017The power of deeper networks for expressing natural functionscited by 35
  8. 2021A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardwarecited by 32
  9. 2020Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generationcited by 27
  10. 2023SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillationcited by 17
  11. 2025STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networkscited by 11
  12. 2024Spiking Convolutional Neural Networks for Text Classificationcited by 10
  13. 2024A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecturecited by 8
  14. 2024Wafer2Spike: Spiking Neural Network for Wafer Map Pattern Classificationcited by 3
  15. 2025Improving Performance of Spike-based Deep Q-Learning using Ternary Neuronscited by 3
  16. 2025New Spiking Architecture for Multi-Modal Decision-Making in Autonomous Vehiclescited by 2
  17. 2017EliXa: A Modular and Flexible ABSA Platformcited by 1

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