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Resources for Modeling Derivation Using Methods from Distributional Semantics

Typ ExperimentData
Titel Resources for Modeling Derivation Using Methods from Distributional Semantics
Autor Sebastian Padó, Aurélie Herbelot, Max Kisselew, Jan Šnajder

Beschreibung

We are interested in modeling and investigating morphological derivation using methods from Distributional Semantics. Most our work for German is based on DErivBase (Zeller et al., 2013), a derivation lexicon that groups 280K lemmas into 17K derivational families. Other resources used in our recent work can be found below in the "Download" section.


Referenz

Sebastian Padó, Aurélie Herbelot, Max Kisselew, Jan Šnajder:
Predictability of Distributional Semantics in Derivational Word Formation.
In: Proceedings of COLING 2016. Osaka, Japan.


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Resources from Predictability of Distributional Semantics in Derivational Word Formation: