NER-Luxury: Named entity recognition for the fashion and luxury domain

TL;DR

This paper introduces a specialized NER model tailored for the fashion and luxury industry, addressing domain-specific challenges with a new taxonomy, dataset, and fine-tuned models, and compares its performance against large language models.

Contribution

It presents a novel luxury-oriented taxonomy, creates a large annotated dataset, and develops fine-tuned NER models specifically for the fashion and luxury domain.

Findings

The models outperform general-purpose large language models in NER tasks.

The dataset contains over 40,000 sentences with hierarchical entity annotations.

Fine-tuned models show promising results in domain-specific NER applications.

Abstract

In this study, we address multiple challenges of developing a named-entity recognition model in English for the fashion and luxury industry, namely the entity disambiguation, French technical jargon in multiple sub-sectors, scarcity of the ESG methodology, and a disparate company structures of the sector with small and medium-sized luxury houses to large conglomerate leveraging economy of scale. In this work, we introduce a taxonomy of 36+ entity types with a luxury-oriented annotation scheme, and create a dataset of more than 40K sentences respecting a clear hierarchical classification. We also present five supervised fine-tuned models NER-Luxury for fashion, beauty, watches, jewelry, fragrances, cosmetics, and overall luxury, focusing equally on the aesthetic side and the quantitative side. In an additional experiment, we compare in a quantitative empirical assessment of the NER performance of our models against the state-of-the-art open-source large language models that show promising results and highlights the benefits of incorporating a bespoke NER model in existing machine learning pipelines.