Compared to verbal predicates, the structure between nominal predicates and their roles in Semantic Role Labeling (SRL) is more flexible and complex. In this paper, some new word-related and syntactic features were explored from various nominal predicate-specific features to capture the structure information for nominal SRL. The experimental results show that the propesed nominal SRL system achieved the performance of 73.99 in Fl-measure on gold parse trees and gold predicates, and outperformed the state-of-the-art nominal SRL. However, the performance dropped to 57.16 in F1-measure on automatic parse trees and automatic predicates. Finally, the training data were augmented with verbal SRL instances to examine whether nominal SRL could benefit from verbal instances. The experimental result show, however, adding verbal SRL instances does indeed improve the performance of nominal SRL, although the improvement is not statistically significant.%在语义角色标注中,相对于动词性谓词,名词性谓词与其角色之间的结构更灵活和复杂.为了更好地捕获这些结构化信息,通过对名词性谓词语义角色标注相关特征集的研究,探索了新的单词特征和句法特征,用于名词性谓词语义角色标注.基于正确句法树和正确谓词识别,中文名词性谓词语义角色标注的F1值达到了73.99,优于目前国内外的同类系统;基于自动句法树和自动谓词识别,性能F1值为57.16.最后,讨论了使用动词性谓词的特征实例来提高名词性谓词SRL的准确率,然而性能的提高并不是很明显.
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