@inproceedings{BrunnerTuWeimeretal.2019,
  author    = {Brunner, Annelen and Tu, Ngoc Duyen Tanja and Weimer, Lukas and Jannidis, Fotis},
  title     = {Deep learning for free indirect representation},
  booktitle = {Preliminary proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019), October 9 - 11, 2019 at Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg},
  url       = {https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_27.pdf},
  pages     = {241 -- 245},
  year      = {2019},
  abstract  = {In this paper, we present our work-inprogress to automatically identify free indirect representation (FI), a type of thought representation used in literary texts. With a deep learning approach using contextual string embeddings, we achieve f1 scores between 0.45 and 0.5 (sentence-based evaluation for the FI category) on two very different German corpora, a clear improvement on earlier attempts for this task. We show how consistently marked direct speech can help in this task. In our evaluation, we also consider human inter-annotator scores and thus address measures of certainty for this difficult phenomenon.},
  subject      = {Deutsch},
  language  = {en}
}