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arXiv Computation and Language By Jordan Lee, Filippos Ventirozos, Abdirahman Abdullahm, Ioanna Nteka, Peter Appleby, Matthew Shardlow

AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction

Read the original on arXiv Computation and Language →

AutoSpecNER is a new expert‑annotated dataset for fine‑grained named entity recognition in vehicle listings, comprising 659 advertisements and over 10,000 entities across 15 categories such as MODEL, ENGINE_SPEC, and BATTERY_CAPACITY. The annotation quality was validated with an inter‑annotator agreement score of 91.5%. Benchmarking shows that DeBERTa achieves a 90% micro‑F1, far surpassing a rule‑based baseline (43%) and the best large language model (77.8%).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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