@inproceedings{Nam2014,
  author    = {Jinseok Nam},
  title     = {Semi-Supervised Neural Networks for Nested Named Entity Recognition},
  series = {Workshop proceedings of the 12th edition of the KONVENS conference},
  url       = {https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-3085},
  pages     = {144 -- 148},
  year      = {2014},
  abstract  = {In this paper, we investigate a semi- supervised learning approach based on neu- ral networks for nested named entity recog- nition on the GermEval 2014 dataset. The dataset consists of triples of a word, a named entity associated with that word in the first-level and one in the second-level. Additionally, the tag distribution is highly skewed, that is, the number of occurrences of certain types of tags is too small. Hence, we present a unified neural network archi- tecture to deal with named entities in both levels simultaneously and to improve gen- eralization performance on the classes that have a small number of labelled examples.},
  language  = {en}
}