Abstract
The key challenge in the attribute-value extraction (AVE) task from e-commerce sites is the scalability to diverse attributes for a large number of products in real-world e-commerce sites. To make AVE scalable to diverse attributes, recent researchers adopted a question-answering (QA)-based approach that additionally inputs the target attribute as a query to extract its values, and confirmed its advantage over a classical approach based on named-entity recognition (NER) on real-word e-commerce datasets. In this study, we argue the scalability of the NER-based approach compared to the QA-based approach, since researchers have compared BERT-based QA-based models to only a weak BiLSTM-based NER baseline trained from scratch in terms of only accuracy on datasets designed to evaluate the QA-based approach. Experimental results using a publicly available real-word dataset revealed that, under a fair setting, BERT-based NER models rival BERT-based QA models in terms of the accuracy, and their inference is faster than the QA model that processes the same product text several times to handle multiple target attributes.- Anthology ID:
- 2023.emnlp-industry.16
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Mingxuan Wang, Imed Zitouni
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 152–159
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-industry.16
- DOI:
- 10.18653/v1/2023.emnlp-industry.16
- Cite (ACL):
- Wei-Te Chen, Keiji Shinzato, Naoki Yoshinaga, and Yandi Xia. 2023. Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction?. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 152–159, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? (Chen et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.emnlp-industry.16.pdf