中文摘要：

汉语框架网的低覆盖率导致汉语句子中存在许多未登录的词元,严重制约着汉语的框架语义分析任务.针对未登录词元的框架识别问题,该文借助同义词词林的词义信息,提出基于平均语义相似度计算及最大熵模型两种方法,采用静态特征与动态特征相结合的特征选择方法.实验证明,这两种方法都能有效地实现未登录词元的框架选择,基于相似度计算的方法（TOP-4）获得78.61％的准确率;基于最大熵的方法结果可达87.29％,同时在新闻语料上达到了75％的准确率.

英文摘要：

The low coverage of Chinese FrameNet leads to many unknown lexical units and restricts the frames se- mantic analysis for Chinese. In order to identify frames for unknown lexical units, this paper proposes two methods based on Tongyici CiLin： the Average Semantic Similarity method and Maximum Entropy （ME-based） method which both combine the static features and dynamic features. Experiments show that the two methods can effectively identify the frame of unknown lexical units： the accuracy of the similarity-based method is 78. 61%considering Top-4 candidates; the Top-1 accuracy of the ME-based method for the same test set is 87.29% （and 75% for anoth- er news texts）.