@inproceedings{yang-etal-2022-spelling,
title = "Spelling Correction using Phonetics in {E}-commerce Search",
author = "Yang, Fan and
Bagheri Garakani, Alireza and
Teng, Yifei and
Gao, Yan and
Liu, Jia and
Deng, Jingyuan and
Sun, Yi",
editor = "Malmasi, Shervin and
Rokhlenko, Oleg and
Ueffing, Nicola and
Guy, Ido and
Agichtein, Eugene and
Kallumadi, Surya",
booktitle = "Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ecnlp-1.9",
doi = "10.18653/v1/2022.ecnlp-1.9",
pages = "63--67",
abstract = "In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., {``}bluetooth sound system{''} vs. {``}blutut sant sistam{''}) with numerous noisy forms and sparse occurrences. In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost. Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9{\%}+ and recall of phonetic errors by 8{\%}+. This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.",
}
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<abstract>In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., “bluetooth sound system” vs. “blutut sant sistam”) with numerous noisy forms and sparse occurrences. In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost. Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9%+ and recall of phonetic errors by 8%+. This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.</abstract>
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%0 Conference Proceedings
%T Spelling Correction using Phonetics in E-commerce Search
%A Yang, Fan
%A Bagheri Garakani, Alireza
%A Teng, Yifei
%A Gao, Yan
%A Liu, Jia
%A Deng, Jingyuan
%A Sun, Yi
%Y Malmasi, Shervin
%Y Rokhlenko, Oleg
%Y Ueffing, Nicola
%Y Guy, Ido
%Y Agichtein, Eugene
%Y Kallumadi, Surya
%S Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F yang-etal-2022-spelling
%X In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., “bluetooth sound system” vs. “blutut sant sistam”) with numerous noisy forms and sparse occurrences. In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost. Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9%+ and recall of phonetic errors by 8%+. This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.
%R 10.18653/v1/2022.ecnlp-1.9
%U https://aclanthology.org/2022.ecnlp-1.9
%U https://doi.org/10.18653/v1/2022.ecnlp-1.9
%P 63-67
Markdown (Informal)
[Spelling Correction using Phonetics in E-commerce Search](https://aclanthology.org/2022.ecnlp-1.9) (Yang et al., ECNLP 2022)
ACL
- Fan Yang, Alireza Bagheri Garakani, Yifei Teng, Yan Gao, Jia Liu, Jingyuan Deng, and Yi Sun. 2022. Spelling Correction using Phonetics in E-commerce Search. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 63–67, Dublin, Ireland. Association for Computational Linguistics.