Cross-lingual event-centered news clustering based on elements semantic correlations of different news

Cross-lingual event-centered news clustering based on elements semantic correlations of different... Cross-lingual event-centered news clustering aims to perform the clustering of news documents written in different languages into groups of documents that describe the same event. In order to solve the problem of similarity computation between bi-lingual documents, this paper propose a new method based on semantic correlations of news elements. First, using bilingual entity lexical and terms co-occurrences in news to acquire the semantic correlation of news elements in different language. Then, we compute the similarity between news in different languages using the GVSM model on this basis. Finally, Spectral Clustering is applied to categorize news stories. Experimental results show our method achieves promising results on the F value. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Cross-lingual event-centered news clustering based on elements semantic correlations of different news

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4838-z
Publisher site
See Article on Publisher Site

Abstract

Cross-lingual event-centered news clustering aims to perform the clustering of news documents written in different languages into groups of documents that describe the same event. In order to solve the problem of similarity computation between bi-lingual documents, this paper propose a new method based on semantic correlations of news elements. First, using bilingual entity lexical and terms co-occurrences in news to acquire the semantic correlation of news elements in different language. Then, we compute the similarity between news in different languages using the GVSM model on this basis. Finally, Spectral Clustering is applied to categorize news stories. Experimental results show our method achieves promising results on the F value.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 7, 2017

References

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