【摘要】 描述了一个基于特征向量的语义角色标注系统,该系统以单一句法分析树作为输入。首先进行预处理,过滤掉极不可能是角色的成分,然后进行角色分类(包括NULL类),最后处理嵌套情况及对中心语义角色去重处理。在优化组合已有特征的基础上,从语法、句型以及搭配角度出发,制定了新的有效的特征;实验表明了新特征的有效性及健壮性。最终在CoNLL-2005 Shared Task开发集和WSJ测试集上分别获得了77.54%和78.75%的F1值,是目前已知的基于单一句法分析中取得的最好性能。更多还原

【Abstract】 A feature-based semantic role labeling system operated on signal syntactic parse is constructed.The system is divided into three sequential tasks:(1) filtering out constituents that represent no semantic arguments with high probabilities,(2) classifying constituents of candidate semantic arguments into the specific categories(including NULL class),and(3) dealing with overlap arguments and constituents all labeled as core-arguments in the post-processing step.Besides combining and optimizing the existing features presented in other work,the paper extracts new features according to knowledge of grammar,pattern and collocation.The experiments show the effectiveness and robustness of the new extracted features,with which the finally SRL system achieves F1 value 77.54% and 78.75% on the development and WSJ test set respectively.As far as we know,it is the best result based on single syntactic parsers on the CoNLL-2005 Shared Task.更多还原