Deep learning for Free Indirect Representation.
Annelen Brunner, Ngoc Duyen Tanja Tu, Lukas Weimer, Fotis Jannidis · Publication Server of the Institute for German Language (Institute for German Language) · 2019
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.