A Clustering and Ranking Based Approach for Multi-document Event Fusion

A complete event description is usually scattered over several sentences and documents, so that how to mine a complete event from several documents or event mentions is an issue currently. This paper proposes an event fusion approach to merge a set of event mentions which distributed over several HTML files into a complete event. Firstly it introduced plain features and structured features into the similarity calculation and applied the hierarchical clustering algorithm to cluster event mentions. Then it proposed an event fusion approach based on a ranking model to merge those argument instances with highest ranking rate in each cluster to form a complete event. The experimental result showed that our approach was effective and could achieve higher accuracy than the baseline.