An experience developing a semantic annotation system in a media group
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
A probabilistic graphical model for brand reputation assessment in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Web news extraction via path ratios
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Information on the World Wide Web is congested with large amounts of news contents. Recommendation, filtering, and summarization of Web news have received much attention in Web intelligence, aiming to find interesting news and summarize concise content for users. In this paper, we present our research on developing the Personalized News Filtering and Summarization system (PNFS). An embedded learning component of PNFS induces a user interest model and recommends personalized news. A keyword knowledge base is maintained and provides a real-time update to reflect the general Web news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. Keywords that capture the main topic of the news are extracted using lexical chains to represent semantic relations between words. An Example run of our PNFS system demonstrates the superiority of this Web intelligence system.