SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction
Anna Hätty, Dominik Schlechtweg, Sabine Schulte im Walde · 2019
We introduce SURel, a novel dataset for German with human-annotated meaning shifts between general-language and domain-specific contexts.We show that meaning shifts of term candidates cause errors in term extraction, and demonstrate that the SURel annotation reflects these errors.Furthermore, we illustrate that SURel enables us to assess optimisations of term extraction techniques when incorporating meaning shifts.