First Story Detection using Multiple Nearest Neighbors

Vuurens, J.B.P.; de Vries, A.P.

First Story Detection using Multiple Nearest Neighbors

Conference paper (2016)

Authors

J.B.P. Vuurens , The Hague University of Applied Sciences

A.P. de Vries Radboud Universiteit Nijmegen

To reference this document use:

http://resolver.tudelft.nl/uuid:0f8e849d-a5e8-4982-a715-297d5e488837

More Info

expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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.