Abstract: Traditional method of Event Detection and Characterization (EDC) regards event detection task as classificalion problem. It makes words as samples to train classifier, which can lead to positive and negative samples of classifier imbalance. Meanwhile, there is data sparseness problem of this method when the corpus is small. This paper didn't classify event using word as samples, but clustered event in judging event types. It adapted self-similarity to convergence the value of Kin K-means algorithm by the guidance of event triggers, and optimized clustering algorithm. hhen, combining with named entity and its comparative position information, the new method further ensures the pinpoint type of event.The new method avoids depending on template of event in tradition methods, and its result of event detection can well be used in automatic text summarization, text retrieval, and topic detection and tracking.

Key words: Event detection, Trigger, Self-similarity, Named entity, Clustering