Paper: Using Cross-Entity Inference to Improve Event Extraction

ACL ID P11-1113
Title Using Cross-Entity Inference to Improve Event Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

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 ex- traction 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.