Personalized E-News Monitoring Agent System for Tracking User-interested Chinese News Events,

Abstract

Numerous paper-based newspapers have been transformed into a digital format and published on the Internet. Digital newspapers are gradually becoming a popular electronic media for conveying information immediately. Google developed a powerful news service, Google news alert, based on the Google news aggregator for tracking user-interested new events utilizing a keywords matching approach. However, this service only monitors and tracks news events using the keyword-matching scheme; consequently, the Google news alert retrieves many irrelevant news events and sends them to users. In other words, the current service cannot monitor news events via a specific news topic; although recall rate is high, the precision rate is low when tracking user-interested news events. Thus, this study presents a novel personalized e-news monitoring agent system that employs the topic-tracking-based approach, improving the flaw of the keyword-based approach, for tracking user-interested news events on Google News site. The proposed scheme simultaneously considers both similarities and the semantic relationships among news topics to track news events. Additionally, to further support the promotion of the accuracy rate in tracking user-interested Chinese news events, the Chinese word segmentation system ECScanner (An Extension Chinese Lexicon Scanner) with new word extension is proposed for the Chinese word segmentation process. Experimental results demonstrated that the proposed scheme, based on topic-based approach, is superior to the keyword-based approach used by Google news alert in terms of precision rate, and retains a high recall rate when tracking user-interested news events. Compared with the conventional Chinese word segmentation system CKIP (Chinese Knowledge Information Processing), experimental results also confirmed that using the proposed ECScanner with novel extension mechanism for new words improves the accuracy rate in tracking user-interested news events.