Abstract: At present, popular methods of event extraction regard event arguments or triggers as drivers, but they may cause positive and negative samples imbalance. Furthermore, there will be data sparseness problem when the corpus is small. This paper proposed an event extraction method driven by event sample. Firstly, features of event samples were extracted from news text sentences to compose the description of candidate event Secondly, event samples and non-event samples of news text were classified through binary classification. Finally, event samples were clustered by hierarchical and k-medoids clustering algorithm to complete event extraction. The method not only overcomes positive and negative samples imbalance and data sparseness problem, but also resolves the limit of pre-defined event types. Experimental results indicate that the proposed method is effective, improves precision and recall of event extraction compared to traditional methods.

Key words: Event sample, Classification, News text, Clustering, Event extraction