This paper proposes a new task of cross-document event extraction and tracking and its evaluation metrics. We identify important person entities which are frequently involved in events as 'centroid entities'. Then we link the events involving the same centroid entity along a time line. We also present a system performing this task and our current approaches to address the main research challenges. We demonstrate that global inference from background knowledge and cross-document event aggregation are crucial to enhance the performance. This new task defines several extensions to the traditional single-document Information Extraction paradigm beyond 'slot filling'.
Heng Ji REF proposes to identify the "centroid entities" which are frequently involved in events and then link the events involving the same centroid entity along a time line.
Cross-document Event Extraction and Tracking: Task, Evaluation, Techniques and Challenges
RANLP
Computer Science