@inproceedings{yuan-etal-2025-semi,
title = "A Semi-supervised Scalable Unified Framework for {E}-commerce Query Classification",
author = "Yuan, Chunyuan and
Zhang, Chong and
Fang, Zhen and
Pang, Ming and
Jiang, Xue and
Peng, Changping and
Lin, Zhangang and
Law, Ching",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.88/",
doi = "10.18653/v1/2025.acl-industry.88",
pages = "1263--1271",
ISBN = "979-8-89176-288-6",
abstract = "Query classification, including multiple subtasks such as intent and category prediction, is a vital part of e-commerce applications. E-commerce queries are usually short and lack context, and the information between labels cannot be used, resulting in insufficient prior information for modeling. Most existing industrial query classification methods rely on users' posterior click behavior to construct training samples, resulting in a Matthew vicious cycle. Furthermore, the subtasks of query classification lack a unified framework, leading to low efficiency for algorithm improvement.In this paper, we propose a novel Semi-supervised Scalable Unified Framework (SSUF), containing multiple enhanced modules to unify the query classification tasks. The knowledge-enhanced module uses world knowledge to enhance query representations and solve the problem of insufficient query information. The label-enhanced module uses label semantics and semi-supervised signals to reduce the dependence on posterior labels. The structure-enhanced module enhances the label representation based on the complex label relations. Each module is highly pluggable, and input features can be added or removed as needed according to each subtask. We conduct extensive offline and online A/B experiments, and the results show that SSUF significantly outperforms the state-of-the-art models."
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<abstract>Query classification, including multiple subtasks such as intent and category prediction, is a vital part of e-commerce applications. E-commerce queries are usually short and lack context, and the information between labels cannot be used, resulting in insufficient prior information for modeling. Most existing industrial query classification methods rely on users’ posterior click behavior to construct training samples, resulting in a Matthew vicious cycle. Furthermore, the subtasks of query classification lack a unified framework, leading to low efficiency for algorithm improvement.In this paper, we propose a novel Semi-supervised Scalable Unified Framework (SSUF), containing multiple enhanced modules to unify the query classification tasks. The knowledge-enhanced module uses world knowledge to enhance query representations and solve the problem of insufficient query information. The label-enhanced module uses label semantics and semi-supervised signals to reduce the dependence on posterior labels. The structure-enhanced module enhances the label representation based on the complex label relations. Each module is highly pluggable, and input features can be added or removed as needed according to each subtask. We conduct extensive offline and online A/B experiments, and the results show that SSUF significantly outperforms the state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T A Semi-supervised Scalable Unified Framework for E-commerce Query Classification
%A Yuan, Chunyuan
%A Zhang, Chong
%A Fang, Zhen
%A Pang, Ming
%A Jiang, Xue
%A Peng, Changping
%A Lin, Zhangang
%A Law, Ching
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F yuan-etal-2025-semi
%X Query classification, including multiple subtasks such as intent and category prediction, is a vital part of e-commerce applications. E-commerce queries are usually short and lack context, and the information between labels cannot be used, resulting in insufficient prior information for modeling. Most existing industrial query classification methods rely on users’ posterior click behavior to construct training samples, resulting in a Matthew vicious cycle. Furthermore, the subtasks of query classification lack a unified framework, leading to low efficiency for algorithm improvement.In this paper, we propose a novel Semi-supervised Scalable Unified Framework (SSUF), containing multiple enhanced modules to unify the query classification tasks. The knowledge-enhanced module uses world knowledge to enhance query representations and solve the problem of insufficient query information. The label-enhanced module uses label semantics and semi-supervised signals to reduce the dependence on posterior labels. The structure-enhanced module enhances the label representation based on the complex label relations. Each module is highly pluggable, and input features can be added or removed as needed according to each subtask. We conduct extensive offline and online A/B experiments, and the results show that SSUF significantly outperforms the state-of-the-art models.
%R 10.18653/v1/2025.acl-industry.88
%U https://aclanthology.org/2025.acl-industry.88/
%U https://doi.org/10.18653/v1/2025.acl-industry.88
%P 1263-1271
Markdown (Informal)
[A Semi-supervised Scalable Unified Framework for E-commerce Query Classification](https://aclanthology.org/2025.acl-industry.88/) (Yuan et al., ACL 2025)
ACL
- Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, and Ching Law. 2025. A Semi-supervised Scalable Unified Framework for E-commerce Query Classification. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1263–1271, Vienna, Austria. Association for Computational Linguistics.