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The objective of this research project is to develop a software toolset that mines a large set of unstructured text archives of human rights abuses. The software tool is designed to discover stories of hidden human rights victims and unidentified perpetrators. These stories do not exist in one document but as fragments of text embedded across multiple documents. Thus, these stories can be identified only when reading across a large number of related documents. The current approach of manually reading to identify such stories is incredibly tedious, time-consuming, unsystematic, and error-prone. Human readers find it difficult to correlate the identity of victims, perpetrators, and details of abuse that reside across multiple documents. Thus, the success of this project has significant implications for the human rights community, as currently there is a lack of adequate tool support for automatically reading and identifying stories from large-scale unstructured text document sets.