Auto-AdvER: Disentangling Condition, History, and Commercial Offers in Automotive Advertisements

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.