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.- Anthology ID:
- W17-8005
- Volume:
- Proceedings of the Biomedical NLP Workshop associated with RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 32–41
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-044-1_005
- DOI:
- 10.26615/978-954-452-044-1_005
- Cite (ACL):
- Natalia Grabar and Thierry Hamon. 2017. Understanding of unknown medical words. In Proceedings of the Biomedical NLP Workshop associated with RANLP 2017, pages 32–41, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Understanding of unknown medical words (Grabar & Hamon, RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-044-1_005