Abstract: This paper presents a dependency relations-based semantic role labeling system，which takes dependency relations as the labeled units.Here，a dependency tree is created from a sequence of labeled dependency relations，from which a number of features are extracted.Moreover，the maximum entropy model is trained to identify and classify the predicates’ semantic roles.In the pre-processing step，a dependency tree is pruned using Xue’s algorithm.With extensive feature engineering，a wide range of features and their combinations are applied.Evaluation on the CoNLL 2008 shared task shows that this method achieves the F1-score of 86.25% on the manually labeled version and the F1-score of 81.66% on the automatically labeled version using MSTParser.

Key words: semantic role labeling, dependency relations, features

摘要： 描述了一个基于依存关系的语义角色标注系统，该系统把依存关系作为语义角色标注的基本单元。通过手工或自动标注出来的依存关系，构造出依存关系树，并从树上抽取特征。用最大熵模型对句中谓词的语义角色进行识别和分类。为了消除不必要的结构化信息，在预处理阶段，依存关系树经过了Xue的剪枝算法处理。通过特征工程，丰富的特征及其组合被应用于系统。最终使用 CoNLL 2008 shared task提供的数据作为训练、开发和测试集，使用手工标注的依存关系，F1值达到了86.25%；使用MSTParser自动产生的依存关系，F1值达到了81.66%。

关键词: 语义角色标注, 依存关系, 特征