Abstract
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.
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CITATION STYLE
Hätty, A., Schlechtweg, D., & Schulte im Walde, S. (2019). SURel: A gold standard for incorporating meaning shifts into term extraction. In *SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics (pp. 1–8). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-1001
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