research-article

Online spelling correction for query completion

Authors:

Bo-June (Paul) HsuAuthors Info & Claims

WWW '11: Proceedings of the 20th international conference on World wide web

Pages 117 - 126

https://doi.org/10.1145/1963405.1963425

Published: 28 March 2011 Publication History

Abstract

In this paper, we study the problem of online spelling correction for query completions. Misspelling is a common phenomenon among search engines queries. In order to help users effectively express their information needs, mechanisms for automatically correcting misspelled queries are required. Online spelling correction aims to provide spell corrected completion suggestions as a query is incrementally entered. As latency is crucial to the utility of the suggestions, such an algorithm needs to be not only accurate, but also efficient.

To tackle this problem, we propose and study a generative model for input queries, based on a noisy channel transformation of the intended queries. Utilizing spelling correction pairs, we train a Markov n-gram transformation model that captures user spelling behavior in an unsupervised fashion. To find the top spell-corrected completion suggestions in real-time, we adapt the A* search algorithm with various pruning heuristics to dynamically expand the search space efficiently. Evaluation of the proposed methods demonstrates a substantial increase in the effectiveness of online spelling correction over existing techniques.

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Cited By

Yoo HNandi AFekete JOmidvar-Tehrani BRong KShraga R(2024)Guided Querying over Videos using Autocompletion SuggestionsProceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics10.1145/3665939.3665964(1-7)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665939.3665964
Du HZhao YTian QWang JWang LLan YLu XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic AnnotationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615119(5321-5325)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615119
Ye DTian BFan JLiu JZhou TChen XLi MMa JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Improving Query Correction Using Pre-train Language Model In Search EnginesProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614930(2999-3008)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614930
Show More Cited By

Index Terms

Online spelling correction for query completion
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Other conferences

WWW '11: Proceedings of the 20th international conference on World wide web

March 2011

840 pages

ISBN:9781450306324

DOI:10.1145/1963405

General Chairs:
S. Sadagopan
IIIT-Bangalore, India
,
Krithi Ramamritham
IIT-Bombay, India
,
Arun Kumar
IBM Research, India
,
M. P. Ravindra
Infosys E & R, India
,
Program Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Kumar
Yahoo! Research, USA

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
The International Institute of Information Technology Bangalore: The International Institute of Information Technology Bangalore

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2011

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WWW '11

WWW '11: 20th International World Wide Web Conference

March 28 - April 1, 2011

Hyderabad, India

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Yoo HNandi AFekete JOmidvar-Tehrani BRong KShraga R(2024)Guided Querying over Videos using Autocompletion SuggestionsProceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics10.1145/3665939.3665964(1-7)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665939.3665964
Du HZhao YTian QWang JWang LLan YLu XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic AnnotationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615119(5321-5325)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615119
Ye DTian BFan JLiu JZhou TChen XLi MMa JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Improving Query Correction Using Pre-train Language Model In Search EnginesProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614930(2999-3008)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614930
Gupta MJoshi MAgrawal P(2023)Deep Learning Methods for Query Auto CompletionAdvances in Information Retrieval10.1007/978-3-031-28241-6_35(341-348)Online publication date: 16-Mar-2023
https://doi.org/10.1007/978-3-031-28241-6_35
Yan XZhang JElahi HJiang MGao H(2021)A Personalized Search Query Generating Method for Safety-Enhanced Vehicle-to-People NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2021.307562670:6(5296-5307)Online publication date: Jun-2021
https://doi.org/10.1109/TVT.2021.3075626
Huang JWang HFan MZhuo ALi YGupta RLiu YShah MRajan STang JPrakash B(2020)Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu MapsProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403318(2677-2685)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403318
Miao YQin JHu SDong YIshikawa YOnizuka M(2020)NGNC: A Flexible and Efficient Framework for Error-Tolerant Query AutocompletionSoftware Foundations for Data Interoperability and Large Scale Graph Data Analytics10.1007/978-3-030-61133-0_8(101-115)Online publication date: 6-Nov-2020
https://doi.org/10.1007/978-3-030-61133-0_8
Li LDeng HChang Y(2020)Query Auto-CompletionQuery Understanding for Search Engines10.1007/978-3-030-58334-7_7(145-170)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_7
Li Y(2020)Query Spelling CorrectionQuery Understanding for Search Engines10.1007/978-3-030-58334-7_5(103-127)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_5
Yamamoto KKawaguchi HTsujino Y(2020)Effect of Dialogs’ Arrangement on Accuracy and Workload for Confirming Input DataHCI International 2020 - Posters10.1007/978-3-030-50726-8_77(587-593)Online publication date: 10-Jul-2020
https://doi.org/10.1007/978-3-030-50726-8_77
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