Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure
Yeong-Su Kim, Seungwoo Lee, Markus Dollmann, Michaela Geierhos · International Journal of Advanced Science and Technology · 2018
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.