arXiv:2606.24387 · v2
AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
摘要
Vehicle advertisements contain rich specification information, but automotive NER resources remain limited. We introduce AutoSpecNER, an expert-annotated dataset for fine-grained entity recognition in vehicle listings. The dataset includes 659 advertisements from a popular car-selling website, with over 10,000 entities annotated across 15 categories, including MODEL, ENGINE_SPEC, and BATTERY_CAPACITY. Annotation quality was validated through inter-annotator agreement, achieving an average score of 91.5%. We benchmark rule-based extraction, fine-tuned transformer encoders, and large language models. DeBERTa achieves the best performance with a 90% micro-F1 score, outperforming the rule-based baseline (43%) and the strongest large language model (77.8%).
作者备注：15 pages, 2 figures, 9 tables,
期刊信息：Proceedings of the 9th International Conference on Natural Language and Speech Processing (ICNLSP-2026), pp. 67-81, 2026