Using cross-entity inference to improve event extraction

Authors:
Yu Hong;Jianfeng Zhang;Bin Ma;Jianmin Yao;Guodong Zhou;Qiaoming Zhu
Affiliations:
Soochow University, Suzhou City, China;Soochow University, Suzhou City, China;Soochow University, Suzhou City, China;Soochow University, Suzhou City, China;Soochow University, Suzhou City, China;Soochow University, Suzhou City, China
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Year:
2011

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Abstract

Event extraction is the task of detecting 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.