Feature Engineering for Chinese Semantic Role Labeling
Huaijun Liu, Wanxiang Che, Ting Liu · Zhongwen xinxi xuebao · 2007
In the natural language processing field,researchers have experienced a growth of interest in semantic role labeling by applying statistical and machine-learning methods.Using rich features is the most important part of semantic parsing system.In this paper,some new effective features and combination features are proposed,such as next word of the constituent,predicate and phrase type combination,predicate class and path combination,and so on.And then we report the experiments on the dataset from Chinese Proposition Bank(CPB).After these new features used,the final system improves the F-Score from 89.76% to 91.31%.The results show that the performance of the system has a statistically significant increase.Therefore it is very important to find better features for semantic role labeling.