Abstract
New Event Detection (NED) involves monitoring chronologically-ordered news streams to automatically detect the stories that report on new events. We compare two stories by finding three cosine similarities based on names, topics and the full text. These additional comparisons suggest treating the NED problem as a binary classification problem with the comparison scores serving as features. The classifier models we learned show statistically significant improvement over the baseline vector space model system on all the collections we tested, including the latest TDT5 collection. © 2005 Association for Computational Linguistics.
Cite
CITATION STYLE
Kumaran, G., & Allan, J. (2005). Using names and topics for new event detection. In HLT/EMNLP 2005 - Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 121–128). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220575.1220591
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
