@InProceedings{baumartz-uslu-mehler:2018:C18-2,
  author    = {Baumartz, Daniel  and  Uslu, Tolga  and  Mehler, Alexander},
  title     = {LTV: Labeled Topic Vector},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico},
  publisher = {Association for Computational Linguistics},
  pages     = {142--145},
  abstract  = {In this paper we present LTV, a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC), an international standard for topic classification in libraries. We introduce nnDDC, a largely language-independent natural network-based classifier for DDC, which we optimized using a wide range of linguistic features to achieve an F-score of 87.4%. To show that our approach is language-independent, we evaluate nnDDC using up to 40 different languages. We derive a topic model based on nnDDC, which generates probability distributions over semantic units for any input on sense-, word- and text-level. Unlike related approaches, however, these probabilities are estimated by means of nnDDC so that each dimension of the resulting vector representation is uniquely labeled by a DDC class. In this way, we introduce a neural network-based Classifier-Induced Semantic Space (nnCISS).},
  url       = {http://www.aclweb.org/anthology/C18-2031}
}