A system for detecting and tracking internet news event

Abstract

News event detection is the task of discovering relevant, yet previously unreported real-life events and reporting it to users in human-readable form, while event tracking aims to automatically assign event labels to news stories when they arrive. A new method and system for performing the event detection and tracking task is proposed in this paper. The event detection and tracking method is based on subject extraction and an improved support vector machine (SVM), in which subject concepts can concisely and precisely express the meaning of a longer text. The improved SVM first prunes the negative examples, reserves and deletes a negative sample according to distance and class label, then trains the new set with SVM to obtain a classifier and maps the SVM outputs into probabilities. The experimental results with the real-world data sets indicate the proposed method is feasible and advanced. © Springer-Verlag Berlin Heidelberg 2005.