@article{1098,
  abstract     = {{An end user generally writes down software requirements in ambiguous expressions using natural language; hence, a software developer attuned to programming language finds it difficult to understand th meaning of the requirements. To solve this problem we define semantic categories for disambiguation and classify/annotate the requirement into the categories by using machine-learning models. We extensively use a language frame closely related to such categories for designing features to overcome the problem of insufficient training data compare to the large number of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming the previous model, REaCT.}},
  author       = {{Kim, Yeong-Su and Lee, Seung-Woo  and Dollmann, Markus and Geierhos, Michaela}},
  issn         = {{2205-8494}},
  journal      = {{International Journal of Software Engineering for Smart Device}},
  keywords     = {{Natural Language Processing, Semantic Annotation, Machine Learning}},
  number       = {{2}},
  pages        = {{1--6}},
  publisher    = {{Global Vision School Publication}},
  title        = {{{Semantic Annotation of Software Requirements with Language Frame}}},
  volume       = {{4}},
  year         = {{2017}},
}