@mastersthesis{Demirel2017,
  type      = {Bachelor Thesis},
  author    = {Demirel, Saner},
  title     = {Spectral Graph Convolutional Networks for Part-of-Speech Tagging},
  url       = {https://nbn-resolving.org/urn:nbn:de:kola-15080},
  institution = {Institut f{\"u}r Computervisualistik},
  school    = {Universit{\"a}t Koblenz, Universit{\"a}tsbibliothek},
  pages     = {v, 30},
  year      = {2017},
  abstract  = {Part-of-Speech tagging is the process of assigning words with similar grammatical properties to a part of speech (PoS). In the English language, PoS-tagging algorithms generally reach very high accuracy. This thesis undertakes the task to test against these accuracies in PoS-tagging as a qualitative measure in classification capabilities for a recently developed neural network model, called graph convolutional network (GCN). The novelty proposed in this thesis is to translate a corpus into a graph as a direct input for the GCN. The experiments in this thesis serve as a proof of concept with room for improvements.},
  language  = {en}
}