AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
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Computer Science > Computation and Language
Title:AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
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: | 13 pages, 2 figures, 7 tables, Pre-print |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.24387 [cs.CL] |
| (or arXiv:2606.24387v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24387
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Filippos Ventirozos [view email][v1] Tue, 23 Jun 2026 10:19:58 UTC (196 KB)
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