@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}},
}

@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}},
}

@inproceedings{1123,
  abstract     = {{Within this paper, we describe the special requirements of a semantic annotation scheme used for biographical event
extraction in the framework of the Europeancollaborative research project Biographe. This annotationscheme supports interlingual search for people due to its multilingual support covering four languages such as English, German, French and Dutch.}},
  author       = {{Geierhos, Michaela and Bouraoui, Jean-Leon and Watrin, Patrick}},
  booktitle    = {{Multilingual Resources, Multilingual Applications. Proceedings of the Conference of the German Society for Computational Linguistics and Language Technology (GSCL) 2011}},
  editor       = {{Hedeland, Hanna and Schmidt, Thomas and Wörner, Kai}},
  issn         = {{0176-599X}},
  keywords     = {{Biographical Event Extraction for Interlingual People Search, Semantic Annotation Scheme}},
  location     = {{Hamburg, Germany}},
  pages        = {{45--50}},
  publisher    = {{University of Hamburg}},
  title        = {{{Towards Multilingual Biographical Event Extraction}}},
  volume       = {{96}},
  year         = {{2011}},
}