Auto-AdvER：面向汽车广告的条件、历史与销售选项标注方案与数据集

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Abstract:This paper presents a case study on the development of Auto-AdvER, a specialised span-labelling schema, along with its corresponding dataset for the textual car advertisement domain. Developed with industry needs in mind, Auto-AdvER is designed to enhance text mining analytics in this domain and contributes a linguistically unique sequence-labelling dataset. We introduce a schema consisting of three labels: 'Condition', 'Historic', and 'Sales Options'. We outline the guiding principles for annotation, describe the methodology for schema development, and present the results of an annotation study demonstrating an inter-annotator agreement of 78%. Furthermore, we compare the performance of various models, including transformer encoder models (BERT, DeBERTaV3) and transformer decoder-based open- and closed-source large language models: Llama, Qwen, GPT, and Gemini, as well as conditional random fields. Our results show that a fine-tuned DeBERTaV3 encoder achieves the highest F1-score, followed closely by conditional random fields, while the large language models reach the highest precision but lower recall. We present this work as a stepping stone towards more fine-grained analysis and discuss Auto-AdvER's potential impact on advertisement analytics, pricing, and customer insights. Potential applications include the analysis of market dynamics and data-driven predictive maintenance.

Comments:	17 Accepted to AACL-IJCNLP 2026 (Main Conference). 17 pages, 4 figure, 12 tables. Code and data: this https URL
Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2412.05655 [cs.CL]
	(or arXiv:2412.05655v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2412.05655 arXiv-issued DOI via DataCite

Submission history

From: Filippos Ventirozos [view email]
[v1] Sat, 7 Dec 2024 14:00:06 UTC (475 KB)
[v2] Mon, 26 Oct 2026 08:56:55 UTC (477 KB)