Article

Contextual search and name disambiguation in email using graphs

Authors:

William W. Cohen, and

Andrew Y. NgAuthors Info & Claims

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

August 2006

Pages 27 - 34

https://doi.org/10.1145/1148170.1148179

Published: 06 August 2006 Publication History

Abstract

Similarity measures for text have historically been an important tool for solving information retrieval problems. In many interesting settings, however, documents are often closely connected to other documents, as well as other non-textual objects: for instance, email messages are connected to other messages via header information. In this paper we consider extended similarity metrics for documents and other objects embedded in graphs, facilitated via a lazy graph walk. We provide a detailed instantiation of this framework for email data, where content, social networks and a timeline are integrated in a structural graph. The suggested framework is evaluated for two email-related problems: disambiguating names in email documents, and threading. We show that reranking schemes based on the graph-walk similarity measures often outperform baseline methods, and that further improvements can be obtained by use of appropriate learning methods.

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Cited By

Mei QLi X(2024)Robust Chinese Short Text Entity Disambiguation Method Based on Feature Fusion and Contrastive LearningInformation10.3390/info1503013915:3(139)Online publication date: 29-Feb-2024
https://doi.org/10.3390/info15030139
Nafa YChen QHou BLi Z(2024)Adaptive deep learning for entity disambiguation via knowledge-based risk analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122342238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122342
Elyashar APuzis RFire M(2021)It Runs in the Family: Unsupervised Algorithm for Alternative Name Suggestion Using Digitized Family TreesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3096670(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3096670
Show More Cited By

Index Terms

Contextual search and name disambiguation in email using graphs
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

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Published In

cover image ACM Conferences

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

August 2006

768 pages

ISBN:1595933697

DOI:10.1145/1148170

General Chair:
Efthimis N. Efthimiadis
University of Washington
,
Program Chairs:
Susan Dumais
Microsoft Research, Redmond
,
David Hawking
CSIRO ICT Centre, Canberra, Australia
,
Kalervo Järvelin,
University of Tampere, Finland

Copyright © 2006 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]

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Published: 06 August 2006

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SIGIR06: The 29th Annual International SIGIR Conference

August 6 - 11, 2006

Washington, Seattle, USA

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

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Cited By

Mei QLi X(2024)Robust Chinese Short Text Entity Disambiguation Method Based on Feature Fusion and Contrastive LearningInformation10.3390/info1503013915:3(139)Online publication date: 29-Feb-2024
https://doi.org/10.3390/info15030139
Nafa YChen QHou BLi Z(2024)Adaptive deep learning for entity disambiguation via knowledge-based risk analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122342238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122342
Elyashar APuzis RFire M(2021)It Runs in the Family: Unsupervised Algorithm for Alternative Name Suggestion Using Digitized Family TreesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3096670(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3096670
Zong CXia RZhang JZong CXia RZhang J(2021)Information ExtractionText Data Mining10.1007/978-981-16-0100-2_10(227-283)Online publication date: 21-Jan-2021
https://doi.org/10.1007/978-981-16-0100-2_10
Arslan EOrhan UTahiroğlu B(2019)Morphological Disambiguation of Turkish with Free-order Co-occurrence StatisticsGümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi10.17714/gumusfenbil.430034Online publication date: 31-Jan-2019
https://doi.org/10.17714/gumusfenbil.430034
Heindorf SScholten YEngels GPotthast M(2019)Debiasing Vandalism Detection Models at WikidataThe World Wide Web Conference10.1145/3308558.3313507(670-680)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313507
Sheng FCao QCai HYao JXie C(2018)GraPUProceedings of the ACM Symposium on Cloud Computing10.1145/3267809.3267811(301-312)Online publication date: 11-Oct-2018
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Wi Tay NYang SLee CKubota N(2018)Ontology-based Adaptive e-Textbook Platform for Student and Machine Co-Learning2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2018.8491480(1-7)Online publication date: Jul-2018
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Naeem MLinggawa IMughal ALutteroth CWeber G(2018)A Smart Email Client Prototype for Effective Reuse of Past RepliesIEEE Access10.1109/ACCESS.2018.28785236(69453-69471)Online publication date: 2018
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Gao NDredze MOard D(2017)Person entity linking in email with NIL detectionJournal of the Association for Information Science and Technology10.5555/3204593.320460368:10(2412-2424)Online publication date: 1-Oct-2017
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