arXiv cs.CL· Jordan Lee, Filippos Ventirozos, Abdirahman Abdullahm, Ioanna Nteka, Peter Appleby, Matthew Shardlow·· 12 小时前AI 评分20
AutoSpecNER：面向车辆配置信息抽取的细粒度命名实体识别数据集
AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
AI 导读
AutoSpecNER 是一个专家标注的车辆配置细粒度命名实体识别数据集，包含来自某热门汽车销售网站的 659 条广告，标注了 MODEL、ENGINE_SPEC、BATTERY_CAPACITY 等 15 类、超过 1 万个实体，标注者间一致率平均达 91.5%。基准测试中 DeBERTa 以 90% micro-F1 取得最佳表现，超过规则基线（43%）和最强的大语言模型（77.8%）。
正文 · 原文
Abstract: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%).
| Comments: | 15 pages, 2 figures, 9 tables, |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.24387 [cs.CL] |
| (or arXiv:2606.24387v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24387 arXiv-issued DOI via DataCite |
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| Journal reference: | Proceedings of the 9th International Conference on Natural Language and Speech Processing (ICNLSP-2026), pp. 67-81, 2026 |
Submission history
From: Filippos Ventirozos [view email]
[v1]
Tue, 23 Jun 2026 10:19:58 UTC (196 KB)
[v2]
Sun, 1 Nov 2026 05:44:04 UTC (201 KB)
来源：arXiv cs.CL · arxiv.org