Joint Modeling for Chinese Event Extraction with Rich Linguistic Features

Joint Modeling for Chinese Event Extraction with Rich Linguistic Features (运用丰富语言学特征的中文事件抽取联合模型)

Chen Chen and Vincent Ng.
Proceedings of the 24th International Conference on Computational Linguistics, pp. 529-544, 2012.

Click here for the PDF version. The talk slides are available here.

Abstract

Compared to the amount of research that has been done on English event extraction, there exists relatively little work on Chinese event extraction. We seek to push the frontiers of supervised Chinese event extraction research by proposing two extension to Li et al.'s (2012) state-of-the-art event extraction system. First, we employ a joint modeling approach to event extraction, aiming to address the error propagation problem inherent in Li et al.'s pipeline system architecture. Second, we investigate a variety of rich knowledge sources for Chinese event extraction that encode knowledge ranging from the character level to the discourse level. Experimental results on the ACE 2005 dataset show that our joint-modeling, knowledge-rich approach significantly outperforms Li et al.'s approach.

Abstract in Chinese

与英文的事件抽取研究相比，对于中文的事件抽取研究工作相对较少。在Li et al.(2012)的基于监督学习的事件抽取系统基础上，我们提出了两个扩展以进一步推动中文事件抽取的研究。首先，我们使用了一个联合模型，以解决Li et al.管道式系统中的错误传播问题。其次，针对中文信息抽取，我们研究了一系列从字符层面到文章层面的特征。在ACE2005数据上的实验结果表明，我们运用丰富语言学特征的联合模型显著地优于Li et al.的方法。

BibTeX entry

@InProceedings{Chen+Ng:12b,
  author = {Chen Chen and Vincent Ng},
  title = {Joint Modeling for Chinese Event Extraction with Rich Linguistic Features},
  booktitle = {Proceedings of the 24th International Conference on Computational Linguistics},
  pages = {529--544},
  year = 2012
}