Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture
Rafael Ehren, Timm Lichte, Laura Kallmeyer, Jakub Waszczuk · 2020
Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation.In this paper, we present a new VID corpus for German and perform a series of VID disambiguation experiments on it.Our best classifier, based on a neural architecture, yields an error reduction across VIDs of 57% in terms of accuracy compared to a simple majority baseline.