short-paper

Query Expansion for Email Search

Authors:

Saar Kuzi,

David Carmel,

Alex Libov,

Ariel RavivAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 849 - 852

https://doi.org/10.1145/3077136.3080660

Published: 07 August 2017 Publication History

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Abstract

This work studies the effectiveness of query expansion for email search. Three state-of-the-art expansion methods are examined: 1) a global translation-based expansion model; 2) a personalized-based word embedding model; 3) the classical pseudo-relevance-feedback model. Experiments were conducted with two mail datasets extracted from a large query log of a Web mail service. Our results demonstrate the significant contribution of query expansion for measuring the similarity between the query and email messages. On the other hand, the contribution of expansion methods for a well trained learning-to-rank scoring function that exploits many relevance signals, was found to be modest.

References

[1]

Adam Berger and John Lafferty 1999. Information Retrieval As Statistical Translation. Proceedings of SIGIR. 222--229.

Digital Library

Google Scholar

[2]

David Carmel, Guy Halawi, Liane Lewin-Eytan, Yoelle Maarek, and Ariel Raviv 2015. Rank by time or by relevance? Revisiting email search Proceedings of CIKM. ACM, 283--292.

Digital Library

Google Scholar

[3]

David Carmel, Liane Lewin-Eytan, ALex Libov, Yoelle Maarek, and Ariel Raviv. 2017. The Demographics of Mail Search and their Application to Query Suggestion Proceedings of WWW. ACM.

Google Scholar

[4]

David Carmel, Liane Lewin-Eytan, ALex Libov, Yoelle Maarek, and Ariel Raviv. 2017. Promoting Relevant Results in Time-Ranked Mail Search Proceedings of WWW. ACM.

Google Scholar

[5]

Claudio Carpineto and Giovanni Romano. 2012. A Survey of Automatic Query Expansion in Information Retrieval. ACM Comput. Surv., Vol. 44, 1 (Jan. 2012), 1:1--1:50.

Digital Library

Google Scholar

[6]

Hang Cui, Ji-Rong Wen, Jian-Yun Nie, and Wei-Ying Ma. 2002. Probabilistic Query Expansion Using Query Logs. In Proceedings of WWW. ACM, 325--332.

Digital Library

Google Scholar

[7]

Nick Craswell Fernando Diaz, Bhaskar Mitra 2016. Query Expansion with Locally-Trained Word Embeddings Proceedings of ACL.

Google Scholar

[8]

Saar Kuzi, Anna Shtok, and Oren Kurland. 2016. Query Expansion Using Word Embeddings. In Proceedings of CIKM. ACM, 1929--1932.

Digital Library

Google Scholar

[9]

Victor Lavrenko and W. Bruce Croft. 2001. Relevance Based Language Models. In Proceedings of SIGIR. ACM, 120--127.

Digital Library

Google Scholar

[10]

Donald Metzler and W. Bruce Croft. 2005. A Markov random field model for term dependencies. Proceedings of SIGIR. ACM, 472--479.

Digital Library

Google Scholar

[11]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013).

Google Scholar

Cited By

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Bassani ETonellotto NPasi G(2023)Personalized Query Expansion with Contextual Word EmbeddingsACM Transactions on Information Systems10.1145/362498842:2(1-35)Online publication date: 20-Sep-2023
https://dl.acm.org/doi/10.1145/3624988
Najafipour SHosseini SHua WKangavari MZhou X(2022)SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.298214834:1(448-461)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TKDE.2020.2982148
Vuong TAndolina SJacucci GRuotsalo T(2021)Does More Context Help? Effects of Context Window and Application Source on Retrieval PerformanceACM Transactions on Information Systems10.1145/347405540:2(1-40)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3474055
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Index Terms

Query Expansion for Email Search
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Citations

Cited By

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Bassani ETonellotto NPasi G(2023)Personalized Query Expansion with Contextual Word EmbeddingsACM Transactions on Information Systems10.1145/362498842:2(1-35)Online publication date: 20-Sep-2023
https://dl.acm.org/doi/10.1145/3624988
Najafipour SHosseini SHua WKangavari MZhou X(2022)SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.298214834:1(448-461)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TKDE.2020.2982148
Vuong TAndolina SJacucci GRuotsalo T(2021)Does More Context Help? Effects of Context Window and Application Source on Retrieval PerformanceACM Transactions on Information Systems10.1145/347405540:2(1-40)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3474055
Bi KMetrikov PLi CByun B(2021)Leveraging User Behavior History for Personalized Email SearchProceedings of the Web Conference 202110.1145/3442381.3450110(2858-2868)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450110
Torbati GYates AWeikum G(2021)You Get What You Chat: Using Conversations to Personalize Search-Based RecommendationsAdvances in Information Retrieval10.1007/978-3-030-72113-8_14(207-223)Online publication date: 27-Mar-2021
https://doi.org/10.1007/978-3-030-72113-8_14
Yusuf NYunus MWahid NSalleh M(2021)A Statistical Linguistic Terms Interrelationship Approach to Query Expansion Based on Terms Selection ValueInformation and Communication Technology and Applications10.1007/978-3-030-69143-1_19(234-244)Online publication date: 14-Feb-2021
https://doi.org/10.1007/978-3-030-69143-1_19
Torbati GYates AWeikum GO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)Personalized Entity Search by Sparse and Scrutable User ProfilesProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378011(427-431)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3378011
Meng YKarimzadehgan MZhuang HMetzler DCaverlee JHu XLalmas MWang W(2020)Separate and Attend in Personal Email SearchProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371775(429-437)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371775
Terolli EErnst PWeikum G(2020)Focused Query Expansion with Entity Cores for Patient-Centric Health SearchThe Semantic Web – ISWC 202010.1007/978-3-030-62419-4_31(547-564)Online publication date: 1-Nov-2020
https://doi.org/10.1007/978-3-030-62419-4_31
Alshalan SAlshalan RAl-Khalifa HSuwaileh RElsayed T(2020)Improving Arabic Microblog Retrieval with Distributed RepresentationsInformation Retrieval Technology10.1007/978-3-030-42835-8_16(185-194)Online publication date: 27-Feb-2020
https://doi.org/10.1007/978-3-030-42835-8_16
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Characterizing Email Search using Large-scale Behavioral Logs and Surveys

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