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Estimating document focus time

Authors:

Ching-Man Au Yeung,

Katsumi TanakaAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 2273 - 2278

https://doi.org/10.1145/2505515.2505655

Published: 27 October 2013 Publication History

Abstract

Temporality is an important characteristic of text documents. While some documents are clearly atemporal, many have temporal character and can be mapped to certain time periods. In this paper, we introduce the problem of estimating focus time of documents. Document focus time is defined as the time to which the content of a document refers to and is considered as a complementary dimension to its creation time or timestamp. We propose several estimators of focus time by utilizing external knowledge bases such as news article collections which contain explicit temporal references. We then evaluate the effectiveness of our methods on diverse datasets of documents about historical events in five countries.

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Cited By

Santana BCampos RAmorim EJorge ASilvano PNunes S(2023)A survey on narrative extraction from textual dataArtificial Intelligence Review10.1007/s10462-022-10338-756:8(8393-8435)Online publication date: 6-Jan-2023
https://dl.acm.org/doi/10.1007/s10462-022-10338-7
Jatowt ASato MDraxl SDuan YCampos RYoshikawa M(2023)Is this news article still relevant? Ranking by contemporary relevance in archival searchInternational Journal on Digital Libraries10.1007/s00799-023-00377-y25:2(197-216)Online publication date: 28-Jul-2023
https://dl.acm.org/doi/10.1007/s00799-023-00377-y
Zhang LJoho HYu H(2022)Semantic Modelling of Document Focus-Time for Temporal Information RetrievalCompanion Proceedings of the Web Conference 202210.1145/3487553.3524668(896-902)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524668
Show More Cited By

Index Terms

Estimating document focus time
1. Applied computing
  1. Document management and text processing
    1. Document capture
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 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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Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

Santana BCampos RAmorim EJorge ASilvano PNunes S(2023)A survey on narrative extraction from textual dataArtificial Intelligence Review10.1007/s10462-022-10338-756:8(8393-8435)Online publication date: 6-Jan-2023
https://dl.acm.org/doi/10.1007/s10462-022-10338-7
Jatowt ASato MDraxl SDuan YCampos RYoshikawa M(2023)Is this news article still relevant? Ranking by contemporary relevance in archival searchInternational Journal on Digital Libraries10.1007/s00799-023-00377-y25:2(197-216)Online publication date: 28-Jul-2023
https://dl.acm.org/doi/10.1007/s00799-023-00377-y
Zhang LJoho HYu H(2022)Semantic Modelling of Document Focus-Time for Temporal Information RetrievalCompanion Proceedings of the Web Conference 202210.1145/3487553.3524668(896-902)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524668
Wang JJatowt AYoshikawa MDiaz FShah CSuel TCastells PJones RSakai T(2021)Event Occurrence Date Estimation based on Multivariate Time Series Analysis over Temporal Document CollectionsProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462885(398-407)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462885
Sato MJatowt ADuan YCampos RYoshikawa MDownie J(2021)Estimating Contemporary Relevance of Past NewsProceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries10.1109/JCDL52503.2021.00019(70-79)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1109/JCDL52503.2021.00019
Spitz AAumiller DSoproni BGertz M(2020)A Versatile Hypergraph Model for Document CollectionsProceedings of the 32nd International Conference on Scientific and Statistical Database Management10.1145/3400903.3400919(1-12)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3400903.3400919
Shahbazi ZByun Y(2019)Computing Focus Time of Paragraph Using Deep Learning2019 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific)10.1109/ITEC-AP.2019.8903832(1-4)Online publication date: May-2019
https://doi.org/10.1109/ITEC-AP.2019.8903832
Duan YJatowt ATanaka K(2019)Discovering Latent Threads in Entity HistoriesData Science and Engineering10.1007/s41019-019-00108-x4:4(336-351)Online publication date: 15-Nov-2019
https://doi.org/10.1007/s41019-019-00108-x
Almquist AJatowt A(2019)Towards Content Expiry Date Determination: Predicting Validity Periods of SentencesAdvances in Information Retrieval10.1007/978-3-030-15712-8_6(86-101)Online publication date: 14-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-15712-8_6
Morbidoni CCucchiarelli AUrsino DDesai BFlesca SZumpano EMasciari ECaroprese L(2018)Leveraging linked entities to estimate focus time of short textsProceedings of the 22nd International Database Engineering & Applications Symposium10.1145/3216122.3216158(282-286)Online publication date: 18-Jun-2018
https://dl.acm.org/doi/10.1145/3216122.3216158
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