{
  "annotation_id": "c66d3d29-ac98-45ca-b0ab-3cf9b0cf961e",
  "date_created": "2026-02-17T05:53:08.603000Z",
  "date_modified": "2026-02-17T05:53:08.603000Z",
  "file_hash": "08c19a3ee97db9b79818884a232cada863aa7af8a5a722049cdfd001384e3017",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.06637",
    "authors": [
      "Abhishek Kumar Mishra",
      "Arya Somasundaram",
      "Anup Das",
      "Nagarajan Kandasamy"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Efficient Aspect Term Extraction using Spiking Neural Network",
    "url": "https://arxiv.org/abs/2601.06637",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a79de607-a76d-44e9-8f99-8f70c6af3c1f",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}