AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
arXiv:2606.24387v1 Announce Type: new 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-t
The proliferation of generative AI models creates a continuous demand for novel and specific datasets to improve their performance across various real-world applications.
This development is crucial for industries reliant on unstructured text data, enabling more sophisticated and automated information extraction and analysis.
The availability of AutoSpecNER enriches the resources for fine-grained named entity recognition in the automotive sector, enhancing the capabilities of AI in this domain.
- · Automotive AI developers
- · Car-selling platforms
- · Data annotation services
- · AI-driven analytics companies
- · Manual data entry specialists
- · Generic NER models
Improved accuracy in extracting vehicle specifications from unstructured advertisements.
Faster and more automated processing of car listings, leading to more efficient marketplaces.
Enhanced competition among automotive retailers leverage advanced AI for pricing, recommendations, and inventory management.
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Read at arXiv cs.CL