Wordform- and class-based prediction of the components of German nominal compounds in an AAC system

A model is presented that predicts German nominal compounds by splitting them into their modifier and head components, instead of trying to predict them as a whole, which leads to an improvement in keystroke savings.

Abstract

In word prediction systems for augmentative and alternative communication (AAC), productive word-formation processes such as compounding pose a serious problem. We present a model that predicts German nominal compounds by splitting them into their modifier and head components, instead of trying to predict them as a whole. The model is improved further by the use of class-based modifier-head bigrams constructed using semantic classes automatically extracted from a corpus. The evaluation shows that the split compound model with class bigrams leads to an improvement in keystroke savings of more than 15% over a no split compound baseline model. We also present preliminary results obtained with a word prediction model integrating compound and simple word prediction.