@inproceedings{guo-etal-2025-learning,
title = "Learning to Rewrite Negation Queries in Product Search",
author = "Guo, Mengtian and
Al-Darabsah, Mutasem and
Teo, Choon Hui and
May, Jonathan and
Agarwal, Tarun and
Bhagat, Rahul",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.49/",
pages = "575--582",
abstract = "In product search, negation is frequently used to articulate unwanted product features or components. Modern search engines often struggle to comprehend negations, resulting in suboptimal user experiences. While various methods have been proposed to tackle negations in search, none of them took the vocabulary gap between query keywords and product text into consideration. In this work, we introduced a query rewriting approach to enhance the performance of product search engines when dealing with queries with negations. First, we introduced a data generation workflow that leverages large language models (LLMs) to extract query rewrites from product text. Subsequently, we trained a Seq2Seq model to generate query rewrite for unseen queries. Our experiments demonstrated that query rewriting yields a 3.17{\%} precision@30 improvement for queries with negations. The promising results pave the way for further research on enhancing the search performance of queries with negations."
}
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%0 Conference Proceedings
%T Learning to Rewrite Negation Queries in Product Search
%A Guo, Mengtian
%A Al-Darabsah, Mutasem
%A Teo, Choon Hui
%A May, Jonathan
%A Agarwal, Tarun
%A Bhagat, Rahul
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F guo-etal-2025-learning
%X In product search, negation is frequently used to articulate unwanted product features or components. Modern search engines often struggle to comprehend negations, resulting in suboptimal user experiences. While various methods have been proposed to tackle negations in search, none of them took the vocabulary gap between query keywords and product text into consideration. In this work, we introduced a query rewriting approach to enhance the performance of product search engines when dealing with queries with negations. First, we introduced a data generation workflow that leverages large language models (LLMs) to extract query rewrites from product text. Subsequently, we trained a Seq2Seq model to generate query rewrite for unseen queries. Our experiments demonstrated that query rewriting yields a 3.17% precision@30 improvement for queries with negations. The promising results pave the way for further research on enhancing the search performance of queries with negations.
%U https://aclanthology.org/2025.coling-industry.49/
%P 575-582
Markdown (Informal)
[Learning to Rewrite Negation Queries in Product Search](https://aclanthology.org/2025.coling-industry.49/) (Guo et al., COLING 2025)
ACL
- Mengtian Guo, Mutasem Al-Darabsah, Choon Hui Teo, Jonathan May, Tarun Agarwal, and Rahul Bhagat. 2025. Learning to Rewrite Negation Queries in Product Search. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 575–582, Abu Dhabi, UAE. Association for Computational Linguistics.