First Story Detection using Multiple Nearest Neighbors

Jeroen BP Vuurens, Arjen P de Vries

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 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 languageEnglish
Title of host publicationProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
EditorsR. Perego, F. Sebastiani
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages845-848
Number of pages4
ISBN (Electronic)978-1-4503-4069-4
DOIs
Publication statusPublished - 2016
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: 17 Jul 201621 Jul 2016

Conference

Conference39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
CountryItaly
CityPisa
Period17/07/1621/07/16

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