摘要： 零形式识别与填充是在语篇上下文中为句中缺失的语义角色寻找填充项，然而采用分类思想预测集合中正确填充项的方法制约了零形式填充的性能。针对该问题，结合启发式规则与决策树算法识别出需要填充内容的零形式，将上下文中填充过框架元素的内容构成候选语集合，并通过改进的SMOTE算法对少数类样本数据进行扩展，解决了候选语集合数据的非平衡问题。在此基础上，借助汉语框架知识库提取语义相似性特征，利用框架元素间的映射关系提升零形式填充效果。实验结果表明，该方法在数据层面对填充样本的非平衡性进行处理，可使最终的F值提高约12%。

关键词: 汉语框架网, 零形式识别与填充, 非平衡数据, 语义特征, 决策树算法

Abstract: Null Instantiation(NI) recognition and filling is the process of finding fillers for missing semantic roles of a given sentence from the context in discourses,but existing methods that use classification to predict correct fillers in the set undermine the performance of NI filling.To address this problem,this paper proposes a new NI recognition and filling method.It combines heuristic rules and the decision tree algorithm to identify NI of to-be-filled contents,and the contents filled with frame elements in the context are collected to form a candidate set.Then an improved SMOTE algorithm is used to extend the minority sample data to solve data imbalance in the candidate set.On this basis,semantic similarity features are extracted from the knowledge base of Chinese FrameNet(CFN),and the mapping relationships between frame elements are used to improve filling performance.Experimental results show that this method can increase the final F value by about 12% by relieving the imbalance of filling samples at the data level.

Key words: Chinese FrameNet(CFN), Null Instantiation(NI) recognition and filling, unbalanced data, semantic feature, Decision Tree(DT) algorithm

中图分类号: 

• TP391