RELATEDNESS MEASUREMENT FOR NEWS ITEMS

摘要：

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.

关键词： News relatedness query ezpansion tezt mining information retrieval

作者: LIN LI XIA HU CHAO XU YI-MING ZHOU

作者单位: School of Computer Science and Engineering, Beihang University, Beijing 100083, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2580-2584

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）