short-paper

Public Access

Term Relevance Feedback for Contextual Named Entity Retrieval

Authors:

Sheikh Muhammad Sarwar,

James AllanAuthors Info & Claims

CHIIR '18: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval

Pages 301 - 304

https://doi.org/10.1145/3176349.3176886

Published: 01 March 2018 Publication History

Abstract

We address the role of a user in Contextual Named Entity Retrieval (CNER), showing (1) that user identification of important context-bearing terms is superior to automated approaches, and (2) that further gains are possible if the user indicates the relative importance of those terms. CNER is similar in spirit to List Question answering and Entity disambiguation. However, the main focus of CNER is to obtain user feedback for constructing a profile for a class of entities on the fly and use that to retrieve entities from free text. Given a sentence, and an entity selected from that sentence, CNER aims to retrieve sentences that have entities similar to query entity. This paper explores obtaining term relevance feedback and importance weighting from humans in order to improve a CNER system. We report our findings based on the efforts of IR researchers as well as crowdsourced workers.

References

[1]

Krisztian Balog, Pavel Serdyukov, and Arjen P de Vries. 2010. Overview of the TREC 2010 entity track. Technical Report. DTIC Document.

[2]

N. J. Belkin, C. Cool, D. Kelly, S.-J. Lin, S. Y. Park, J. Perez-Carballo, and C. Sikora. 2001. Iterative Exploration, Design and Evaluation of Support for Query Reformulation in Interactive Information Retrieval. Inf. Process. Manage. (2001).

Digital Library

[3]

Bhavana Bharat Dalvi, Jamie Callan, and William W. Cohen. Entity List Completion Using Set Expansion Techniques. In TREC'10.

[4]

Hoa Trang Dang, Jimmy Lin, and Diane Kelly. Overview of the TREC 2006 Question Answering Track. In TREC'06.

[5]

Gianluca Demartini, Tereza Iofciu, and Arjen P. De Vries. Overview of the INEX 2009 Entity Ranking Track. In INEX'09.

Digital Library

[6]

Jenny Rose Finkel, Trond Grenager, and Christopher Manning. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In ACL'05.

[7]

Diane Kelly and Xin Fu. Elicitation of Term Relevance Feedback: An Investigation of Term Source and Context. In SIGIR'06.

Digital Library

[8]

Jürgen Koenemann and Nicholas J. Belkin. A Case for Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness. In CHI'96.

Digital Library

[9]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and Their Compositionality. In NIPS'13.

Digital Library

[10]

Yael Nemeth, Bracha Shapira, and Meirav Taeib-Maimon. Evaluation of the Real and Perceived Value of Automatic and Interactive Query Expansion. In SIGIR'04.

Digital Library

[11]

Ian Ruthven. Re-examining the Potential Effectiveness of Interactive Query Expansion. In SIGIR'03.

Digital Library

[12]

Yaming Sun, Lei Lin, Duyu Tang, Nan Yang, Zhenzhou Ji, and Xiaolong Wang. Modeling Mention, Context and Entity with Neural Networks for Entity Disambiguation. In IJCAI'15.

Digital Library

[13]

Ellen M Voorhees and Hoa Trang Dang. Overview of the TREC 2005 Question Answering Track. In TREC'05.

[14]

P.C. Wankat. 2002. The Effective, Efficient Professor: Teaching, Scholarship, and Service. Allyn and Bacon.

[15]

John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. Towards Universal Paraphrastic Sentence Embeddings. In ICLR'16.

Cited By

Jafarzadeh PAmirmahani ZEnsan F(2024)Learning contextual representations for entity retrievalApplied Intelligence10.1007/s10489-024-05430-054:19(8820-8840)Online publication date: 4-Jul-2024
https://doi.org/10.1007/s10489-024-05430-0
Sheetrit ERaiber FKurland OYoshioka MKiseleva JAliannejadi M(2023)Entity-Based Relevance Feedback for Document RetrievalProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605128(177-187)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605128
Khan OJónsson BZahálka JRudinac SWorring MCheng WKankanhalli MWang MChu WLiu JWorring M(2021)Impact of Interaction Strategies on User Relevance FeedbackProceedings of the 2021 International Conference on Multimedia Retrieval10.1145/3460426.3463663(590-598)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1145/3460426.3463663
Show More Cited By

Index Terms

Term Relevance Feedback for Contextual Named Entity Retrieval
1. Information systems
  1. Information retrieval

Recommendations

Learning contextual representations for entity retrieval
Abstract
In this paper, we introduce Contextual Entity Ranking (CoER) for the task of entity retrieval. CoER utilizes a textual knowledge graph to learn entities’ representations that are contextualized based on a given query. With these contextual ...
Document-based and term-based linear methods for pseudo-relevance feedback

Query expansion is a successful approach for improving Information Retrieval effectiveness. This work focuses on pseudo-relevance feedback (PRF) which provides an automatic method for expanding queries without explicit user feedback. These techniques ...
Entity linking and retrieval
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

This full-day tutorial presents a comprehensive introduction to entity linking and retrieval. Part I provides a detailed overview of entity linking: identifying and disambiguating entity occurrences in unstructured text. Part II focuses on entity ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CHIIR '18: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval

March 2018

402 pages

ISBN:9781450349253

DOI:10.1145/3176349

General Chairs:
Chirag Shah
Rutgers University, USA
,
Nicholas J. Belkin
Rutgers University, USA
,
Program Chairs:
Katriina Byström
Oslo Metropolitan University, Norway
,
Jeff Huang
Brown University, USA
,
Falk Scholer
RMIT University, Australia

Copyright © 2018 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]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

NSF

Conference

CHIIR '18

Sponsor:

SIGIR

CHIIR '18: Conference on Human Information Interaction and Retrieval

March 11 - 15, 2018

NJ, New Brunswick, USA

Acceptance Rates

CHIIR '18 Paper Acceptance Rate 22 of 57 submissions, 39%;

Overall Acceptance Rate 55 of 163 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
213
Total Downloads

Downloads (Last 12 months)38
Downloads (Last 6 weeks)6

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jafarzadeh PAmirmahani ZEnsan F(2024)Learning contextual representations for entity retrievalApplied Intelligence10.1007/s10489-024-05430-054:19(8820-8840)Online publication date: 4-Jul-2024
https://doi.org/10.1007/s10489-024-05430-0
Sheetrit ERaiber FKurland OYoshioka MKiseleva JAliannejadi M(2023)Entity-Based Relevance Feedback for Document RetrievalProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605128(177-187)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605128
Khan OJónsson BZahálka JRudinac SWorring MCheng WKankanhalli MWang MChu WLiu JWorring M(2021)Impact of Interaction Strategies on User Relevance FeedbackProceedings of the 2021 International Conference on Multimedia Retrieval10.1145/3460426.3463663(590-598)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1145/3460426.3463663
Graef RKlier MKluge KZolitschka J(2020)Human-machine collaboration in online customer service – a long-term feedback-based approachElectronic Markets10.1007/s12525-020-00420-9Online publication date: 6-May-2020
https://doi.org/10.1007/s12525-020-00420-9
Reyes-Magaña JBel-Enguix GGómez-Adorno HSierra G(2019)A Lexical Search Model based on word association normsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-17901036:5(4587-4597)Online publication date: 14-May-2019
https://doi.org/10.3233/JIFS-179010
Kuang NLeung C(2019)Performance Effectiveness of Multimedia Information Search Using the Epsilon-Greedy Algorithm2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)10.1109/ICMLA.2019.00160(929-936)Online publication date: Dec-2019
https://doi.org/10.1109/ICMLA.2019.00160

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents