Conference
Automatic clustering of news reports
| Τίτλος: | Automatic clustering of news reports |
|---|---|
| Συγγραφείς: | Azzopardi, Joel, 5th Computer Science Annual Workshop (CSAW’07) |
| Στοιχεία εκδότη: | University of Malta. Faculty of ICT |
| Έτος έκδοσης: | 2007 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Document clustering, Cluster analysis -- Data processing, Cluster analysis -- Computer programs, News Web sites |
| Περιγραφή: | The automatic clustering of news reports from various web-based news sites into clusters according to the event they cover serves not only to facilitate browsing of news reports by a users but may also serve as an initial stage in other complex systems such as Multi-Document Summarization systems or Document Fusion systems. In contrast to the usual scenarios of document clustering whereby the document collections are static or quasi-static, news sites are continuously updated with re- ports concerning new events. Here, we present a News Report Clustering system which is able to receive a stream of news reports which it clusters on the fly according to the event they cover. New clusters are automat- ically created as necessary for news reports which are covering ‘new’, previously unreported events. We compare the results of our system to the results produced by a standard K-Means clustering system, and we show that our system performs significantly better than the standard K- Means system even though the K-Means system was supplied with the correct number of clusters that should be produced. In fact, our clustering system obtained an average of 11.95% better recall, 28.68% better precision and 0.89% less fallout than the standard K-Means clustering system. ; peer-reviewed |
| Τύπος εγγράφου: | conference object |
| Γλώσσα: | English |
| Relation: | https://www.um.edu.mt/library/oar//handle/123456789/22577 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar//handle/123456789/22577 |
| Rights: | info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Αριθμός Καταχώρησης: | edsbas.95D49121 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.um.edu.mt/library/oar//handle/123456789/22577# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic clustering of news reports – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Azzopardi%2C+Joel%22">Azzopardi, Joel</searchLink><br /><searchLink fieldCode="AR" term="%225th+Computer+Science+Annual+Workshop+%28CSAW%2707%29%22">5th Computer Science Annual Workshop (CSAW’07)</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: University of Malta. Faculty of ICT – Name: DatePubCY Label: Publication Year Group: Date Data: 2007 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Malta: OAR@UM / L-Università ta' Malta – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Document+clustering%22">Document clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+--+Data+processing%22">Cluster analysis -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+--+Computer+programs%22">Cluster analysis -- Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22News+Web+sites%22">News Web sites</searchLink> – Name: Abstract Label: Description Group: Ab Data: The automatic clustering of news reports from various web-based news sites into clusters according to the event they cover serves not only to facilitate browsing of news reports by a users but may also serve as an initial stage in other complex systems such as Multi-Document Summarization systems or Document Fusion systems. In contrast to the usual scenarios of document clustering whereby the document collections are static or quasi-static, news sites are continuously updated with re- ports concerning new events. Here, we present a News Report Clustering system which is able to receive a stream of news reports which it clusters on the fly according to the event they cover. New clusters are automat- ically created as necessary for news reports which are covering ‘new’, previously unreported events. We compare the results of our system to the results produced by a standard K-Means clustering system, and we show that our system performs significantly better than the standard K- Means system even though the K-Means system was supplied with the correct number of clusters that should be produced. In fact, our clustering system obtained an average of 11.95% better recall, 28.68% better precision and 0.89% less fallout than the standard K-Means clustering system. ; peer-reviewed – Name: TypeDocument Label: Document Type Group: TypDoc Data: conference object – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://www.um.edu.mt/library/oar//handle/123456789/22577 – Name: URL Label: Availability Group: URL Data: https://www.um.edu.mt/library/oar//handle/123456789/22577 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. – Name: AN Label: Accession Number Group: ID Data: edsbas.95D49121 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.95D49121 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Document clustering Type: general – SubjectFull: Cluster analysis -- Data processing Type: general – SubjectFull: Cluster analysis -- Computer programs Type: general – SubjectFull: News Web sites Type: general Titles: – TitleFull: Automatic clustering of news reports Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Azzopardi, Joel – PersonEntity: Name: NameFull: 5th Computer Science Annual Workshop (CSAW’07) IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2007 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |