Explicit Attribute Extraction in e-Commerce Search
Robyn Loughnane, Jiaxin Liu, Zhilin Chen, Zhiqi Wang, Joseph Giroux, Tianchuan Du, Benjamin Schroeder, Weiyi Sun
Abstract
This paper presents a model architecture and training pipeline for attribute value extraction from search queries. The model uses weak labels generated from customer interactions to train a transformer-based NER model. A two-stage normalization process is then applied to deal with the problem of a large label space: first, the model output is normalized onto common generic attribute values, then it is mapped onto a larger range of actual product attribute values. This approach lets us successfully apply a transformer-based NER model to the extraction of a broad range of attribute values in a real-time production environment for e-commerce applications, contrary to previous research. In an online test, we demonstrate business value by integrating the model into a system for semantic product retrieval and ranking.- Anthology ID:
- 2024.ecnlp-1.13
- Original:
- 2024.ecnlp-1.13v1
- Version 2:
- 2024.ecnlp-1.13v2
- Volume:
- Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Shervin Malmasi, Besnik Fetahu, Nicola Ueffing, Oleg Rokhlenko, Eugene Agichtein, Ido Guy
- Venues:
- ECNLP | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 125–135
- Language:
- URL:
- https://aclanthology.org/2024.ecnlp-1.13
- DOI:
- Cite (ACL):
- Robyn Loughnane, Jiaxin Liu, Zhilin Chen, Zhiqi Wang, Joseph Giroux, Tianchuan Du, Benjamin Schroeder, and Weiyi Sun. 2024. Explicit Attribute Extraction in e-Commerce Search. In Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024, pages 125–135, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Explicit Attribute Extraction in e-Commerce Search (Loughnane et al., ECNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2024.ecnlp-1.13.pdf