Minimally Supervised Chinese Event Extraction from Multiple Views

Abstract

Although several semi-supervised learning models have been proposed for English event extraction, there are few successful stories in Chinese due to its special characteristics. In this article, we propose a novel minimally supervised model for Chinese event extraction from multiple views. Besides the traditional pattern similarity view (PSV), a semantic relationship view (SRV) is introduced to capture the relevant event mentions from relevant documents. Moreover, a morphological structure view (MSV) is incorporated to both infer more positive patterns and help filter negative patterns via morphological structure similarity. An evaluation of the ACE 2005 Chinese corpus shows that our minimally supervised model significantly outperforms several strong baselines.