Abstract: Argument identification plays an important role for argument classification task in semantic role labeling.According to the headwords of the constituents，this paper researches on argument identification algorithm.The experiment shows that 98.78% of arguments on train set and 97.17% on test set are identified.At the same time，most of NULL arguments are pruned.The existed features are re-combined and optimized to capture more useful information.A SVM classifier is used in the semantic role labeling system，which took syntactic constituents as labeled units.The F1-score of SRL on test set achieves 77.84%.So far as it is known，it is one of the best result based on single syntactic parser in literatures.

摘要： 语义角色标注中论元识别的结果对论元分类任务起着很重要的作用。以句法成分的中心词为依据，对论元识别算法进行研究，在训练集上识别出了98.78%的论元，在测试集识别出了97.17%的论元，并大大减少了不承担角色的训练样例。在此基础上以句法成分为标注单元，在自动句法分析上抽取和组合有用的特征，用支持向量机的方法进行学习分类，在测试集上获得77.84%的F1值。此结果是目前报告的基于单一句法分析的最好结果之一。