Coreference Resolution with Integrated Multiple Background Semantic Knowledge
Sheng Li · Zhongwen xinxi xuebao · 2009
The coreference resolution is an important subtask of information extraction.Recently statistical machine learning methods have been substantially attempted for this issue with some achievements.In this paper,we try to integrate the background semantic knowledge,which is a new subject being introduced in every field of NLP nowadays,into the classical pairwise classification framework for coreference resolution.We extract background knowledge from WordNet and Wikipedia,and exploit the semantic role labeling,general pattern knowledge and the context of mention as well.In the experiment,the feature selection algorithm is employed to decide the best features set,on which the maximum entropy model and SVM model are compared for their performance.The experimental results on ACE dataset exhibit the improvement of coreference resolution after adding selected background semantic knowledge.