First Story Detection using Multiple Nearest Neighbors

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First Story Detection using Multiple Nearest Neighbors

Conference Paper (2016)

Author(s)

Jeroen BP Vuurens (TU Delft - Electrical Engineering, Mathematics and Computer Science, De Haagse Hogeschool)

Arjen P de Vries (Radboud Universiteit Nijmegen)

Research Group

Multimedia Computing

DOI related publication

https://doi.org/10.1145/2911451.2914761 Final published version

To reference this document use

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

More Info

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Publication Year

2016

Language

English

Research Group

Multimedia Computing

Pages (from-to)

845-848

Publisher

ACM

ISBN (electronic)

978-1-4503-4069-4

Event

39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 (2016-07-17 - 2016-07-21), Pisa, Italy

Downloads counter

208

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.