@InProceedings{grabar-hamon:2017:BioNLP,
  author    = {Grabar, Natalia  and  Hamon, Thierry},
  title     = {Understanding of unknown medical words},
  booktitle = {Proceedings of the Biomedical NLP Workshop associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {32--41},
  abstract  = {We assume that unknown words with internal structure (affixed words or
	compounds) can provide speakers with linguistic cues as for their meaning, and
	thus help their decoding and understanding. To verify this hypothesis, we
	propose to work with a set of French medical words. These words are annotated
	by five annotators. Then, two kinds of analysis are performed: analysis of the
	evolution of understandable and non-understandable words (globally and
	according to some suffixes) and analysis of clusters created with unsupervised
	algorithms on basis of linguistic and extra-linguistic features of the studied
	words. Our results suggest that, according to linguistic sensitivity of
	annotators, technical words can be decoded and become understandable. As for
	the clusters, some of them distinguish between understandable and
	non-understandable words. Resources built in this work will be made freely
	available for the research purposes.},
  url       = {https://doi.org/10.26615/978-954-452-044-1_005}
}