Using Cross-Entity Inference to Improve Event Extraction

Yu Hong,  Jianfeng Zhang,  Bin Ma,  Jianmin Yao,  Guodong Zhou,  Qiaoming Zhu
Soochow University


Abstract

Event extraction is the task to detect certain specified types of events that are mentioned in the source language data. The state-of-the-art research on the task is transductive inference (e.g. cross-event inference). In this paper, we propose a new method of event extraction by well using cross-entity inference. In contrast to previous inference methods, we regard entity-type consistency as key feature to predict event mentions. We adopt this inference method to improve the traditional sentence-level event extraction system. Experiments show that we can get 8.6% gain in trigger (event) identification, and more than 11.8% gain for argument (role) classification in ACE event extraction.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-1113.pdf