First Story Detection using Multiple Nearest Neighbors

Jeroen BP Vuurens; Arjen P de Vries

doi:10.1145/2911451.2914761

First Story Detection using Multiple Nearest Neighbors

Jeroen BP Vuurens, Arjen P de Vries

Multimedia Computing

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

Abstract

First Story Detection (FSD) systems aim to identify those news articles that discuss an event that was not reported before. Recent work on FSD has focussed almost exclusively on efficiently detecting documents that are dissimilar from their nearest neighbor. We propose a novel FSD approach that is more effective, by adapting a recently proposed method for news summarization based on 3-nearest neighbor clustering. We show that this approach is more effective than a baseline that uses dissimilarity of an individual document from its nearest neighbor.

Original language	English
Title of host publication	Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Editors	R. Perego, F. Sebastiani
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	845-848
Number of pages	4
ISBN (Electronic)	978-1-4503-4069-4
DOIs	https://doi.org/10.1145/2911451.2914761
Publication status	Published - 2016
Event	39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy Duration: 17 Jul 2016 → 21 Jul 2016

Conference

Conference	39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
Country/Territory	Italy
City	Pisa
Period	17/07/16 → 21/07/16

Access to Document

10.1145/2911451.2914761

Cite this

@inproceedings{0f8e849da5e84982a715297d5e488837,

title = "First Story Detection using Multiple Nearest Neighbors",

abstract = "First Story Detection (FSD) systems aim to identify those news articles that discuss an event that was not reported before. Recent work on FSD has focussed almost exclusively on efficiently detecting documents that are dissimilar from their nearest neighbor. We propose a novel FSD approach that is more effective, by adapting a recently proposed method for news summarization based on 3-nearest neighbor clustering. We show that this approach is more effective than a baseline that uses dissimilarity of an individual document from its nearest neighbor.",

author = "Vuurens, {Jeroen BP} and {de Vries}, {Arjen P}",

year = "2016",

doi = "10.1145/2911451.2914761",

language = "English",

pages = "845--848",

editor = "R. Perego and F. Sebastiani",

booktitle = "Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 ; Conference date: 17-07-2016 Through 21-07-2016",

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Vuurens, JBP & de Vries, AP 2016, First Story Detection using Multiple Nearest Neighbors. in R Perego & F Sebastiani (eds), Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. Association for Computing Machinery (ACM), New York, pp. 845-848, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, 17/07/16. https://doi.org/10.1145/2911451.2914761

First Story Detection using Multiple Nearest Neighbors. / Vuurens, Jeroen BP; de Vries, Arjen P.
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ed. / R. Perego; F. Sebastiani. New York: Association for Computing Machinery (ACM), 2016. p. 845-848.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - First Story Detection using Multiple Nearest Neighbors

AU - Vuurens, Jeroen BP

AU - de Vries, Arjen P

PY - 2016

Y1 - 2016

N2 - First Story Detection (FSD) systems aim to identify those news articles that discuss an event that was not reported before. Recent work on FSD has focussed almost exclusively on efficiently detecting documents that are dissimilar from their nearest neighbor. We propose a novel FSD approach that is more effective, by adapting a recently proposed method for news summarization based on 3-nearest neighbor clustering. We show that this approach is more effective than a baseline that uses dissimilarity of an individual document from its nearest neighbor.

AB - First Story Detection (FSD) systems aim to identify those news articles that discuss an event that was not reported before. Recent work on FSD has focussed almost exclusively on efficiently detecting documents that are dissimilar from their nearest neighbor. We propose a novel FSD approach that is more effective, by adapting a recently proposed method for news summarization based on 3-nearest neighbor clustering. We show that this approach is more effective than a baseline that uses dissimilarity of an individual document from its nearest neighbor.

U2 - 10.1145/2911451.2914761

DO - 10.1145/2911451.2914761

M3 - Conference contribution

SP - 845

EP - 848

BT - Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

A2 - Perego, R.

A2 - Sebastiani, F.

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016

Y2 - 17 July 2016 through 21 July 2016

ER -

First Story Detection using Multiple Nearest Neighbors

Abstract

Conference

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