Title :
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
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;
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
DOI :
10.1109/ICMLC.2008.4620843
