Chinese coreference resolution with ensemble learning
Wenbo Pang, Xiaozhong Fan · 2009
Coreference resolution has been shown to be beneficial in many natural language processing (NLP) applications. In the past decades, various strategies were proposed to address this problem. In order to employ these different strategies in a single model, we glue part of them together with an ensemble learning method based on Maximum Entropy (ME). The different coreference resolution strategies combined in this paper include mention-ranking model, mention-entity model and graph-cut-based model, which are high-quality methods today. The experiments on ACE 2004 Chinese data show that the performance of the proposed method is better than those of three basic models, and improve the coreference resolution effectively.