Semantic Concept Discovery over Event Databases

In this paper, we study the problem of identifying certain types of concept (e.g., persons, organizations, topics) for a given analysis question with the goal of assisting a human analyst in writing a deep analysis report. We consider a case where we have a large event database describing events and their associated news articles along with meta-data describing various event attributes such as people and organizations involved and the topic of the event. We describe the use of semantic technologies in question understanding and deep analysis of the event database, and show a detailed evaluation of our proposed concept discovery techniques using reports from Human Rights Watch organization and other sources. Our study finds that combining our neural network based semantic term embeddings over structured data with an index-based method can significantly outperform either method alone.