TY - JOUR AB - A user generally writes software requirements in ambiguous and incomplete form by using natural language; therefore, a software developer may have difficulty in clearly understanding what the meanings are. To solve this problem with automation, we propose a classifier for semantic annotation with manually pre-defined semantic categories. To improve our classifier, we carefully designed syntactic features extracted by constituency and dependency parsers. Even with a small dataset and a large number of classes, our proposed classifier records an accuracy of 0.75, which outperforms the previous model, REaCT. AU - Kim, Yeongsu AU - Lee, Seungwoo AU - Dollmann, Markus AU - Geierhos, Michaela ID - 2331 JF - International Journal of Advanced Science and Technology KW - Software Engineering KW - Natural Language Processing KW - Semantic Annotation KW - Machine Learning KW - Feature Engineering KW - Syntactic Structure SN - 2005-4238 TI - Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure VL - 112 ER -