Publication

A Domain-adapted Dependency Parser for German Clinical Text

Elif Kara, Tatjana Zeen, Aleksandra Gabryszak, Klemens Budde, Danilo Schmidt, Roland Roller

In: Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018). Konferenz zur Verarbeitung natürlicher Sprache (KONVENS-2018) Vienna Austria KONVENS 9/2018.

Abstract

In this work, we present a syntactic parser specialized for German clinical data. Our model, trained on a small gold standard nephrological dataset, outperforms the default German model of Stanford CoreNLP in parsing nephrology documents in respect to LAS (74.64 vs. 42.15). Moreover, re-training the default model via domain adaptation to nephrology leads to further improvements on nephrology data (78.96). We also show that our model performs well on fictitious clinical data from other subdomains (69.69).

Projekte

Weitere Links

konvens-dependency-tree.pdf (pdf, 139 KB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz