@article{2331,
  abstract     = {{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.}},
  author       = {{Kim, Yeongsu  and Lee, Seungwoo and Dollmann, Markus and Geierhos, Michaela}},
  issn         = {{2207-6360}},
  journal      = {{International Journal of Advanced Science and Technology}},
  keywords     = {{Software Engineering, Natural Language Processing, Semantic Annotation, Machine Learning, Feature Engineering, Syntactic Structure}},
  pages        = {{123--136}},
  publisher    = {{SERSC Australia}},
  title        = {{{Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure}}},
  doi          = {{10.14257/ijast.2018.112.12}},
  volume       = {{112}},
  year         = {{2018}},
}