In this paper, we describe our submission to the ‘Text Complexity DE Challenge 2022’ shared task on predicting the complexity of German sentences. We compare performance of different feature-based regression architectures and transformer language models. Our best candidate is a fine-tuned German Distilbert model that ignores linguistic features of the sentences. Our model ranks 7th place in the shared task.

The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.