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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2024.ecnlp-1.13.pdf