Relatedness measurement for news items

Author

Li, Lin ; Hu, Xia ; Xu, Chao ; Zhou, Yi-ming

Author_Institution

Sch. of Comput. Sci. & Eng., Beihang Univ., Beijing

Volume

5

fYear

2008

fDate

12-15 July 2008

Firstpage

2580

Lastpage

2584

Abstract

This paper proposes a method to extract related news items with respect to a given piece of news from a collection of news items. The related news items include not only the news items with the same topic as the given one but also those implicitly related. In order to find truly related news items, the paper proposes a new query expansion method based on WordNet with the existing word co-occurrence searching method. The method can be used in event prediction and as a tool for information extraction. A benchmark data set with BBC news items is built up to test our idea. Experiments show a high precision and recall rate and a stable performance with different test news items.

Keywords

data mining; query processing; text analysis; WordNet; information extraction; news items collection; query expansion method; word cooccurrence searching method; Benchmark testing; Chaos; Computer science; Cybernetics; Data mining; Feedback; Information retrieval; Machine learning; Text mining; Thesauri; News relatedness; information retrieval; query expansion; text mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620843

Filename

4620843