Adjectives such as heavy (as in heavy rain) and windy (as in windy day) provide possible values for the attributes intensity and climate, respectively. The attributes themselves are not overtly realized and are in this sense implicit. While these attributes can be easily inferred by humans, their automatic classification poses a challenging task for computational models. We present the following contributions: (1) We gain new insights into the attribute selection task for German. More specifically, we develop computational models for this task that are able to generalize to unseen data. Moreover, we show that classification accuracy depends, inter alia, on the degree of polysemy of the lexemes involved, on the generalization potential of the training data and on the degree of semantic transparency of the adjective-noun pairs in question. (2) We provide the first resource for computational and linguistic experiments with German adjective-noun pairs that can be used for attribute selection and related tasks. In order to safeguard against unwelcome memorization effects, we present an automatic data augmentation method based on a lexical resource that can increase the size of the training data to a large extent.

As in many experimental sciences, reproducibility of experiments has gained ever more attention in the NLP community. This paper presents our reproduction efforts of an earlier study of automatic essay scoring (AES) for determining the proficiency of second language learners in a multilingual setting. We present three sets of experiments with different objectives. First, as prescribed by the LREC 2020 REPROLANG shared task, we rerun the original AES system using the code published by the original authors on the same dataset. Second, we repeat the same experiments on the same data with a different implementation. And third, we test the original system on a different dataset and a different language. Most of our findings are in line with the findings of the original paper. Nevertheless, there are some discrepancies between our results and the results presented in the original paper. We report and discuss these differences in detail. We further go into some points related to confirmation of research findings through reproduction, including the choice of the dataset, reporting and accounting for variability, use of appropriate evaluation metrics, and making code and data available. We also discuss the varying uses and differences between the terms reproduction and replication, and we argue that reproduction, the confirmation of conclusions through independent experiments in varied settings is more valuable than exact replication of the published values.

GermaNet (Henrich and Hinrichs, 2010; Hamp and Feldweg, 1997) is a comprehensive wordnet of Standard German spoken in the Federal Republic of Germany. The GermaNet team aims at modelling the basic vocabulary of the language. German is an official language or a minority language in many countries. It is an official language in Austria, Germany and Switzerland, each with its own codified standard variety (Auer, 2014, p. 21), and also in Belgium, Liechtenstein, and Luxemburg. German is recognized as a minority language in thirteen additional countries, including Brasil, Italy, Poland, and Russia. However, the different standard varieties of German are currently not represented in GermaNet. With this project, we make a start on changing this by including one variety, namely Swiss Standard German, into GermaNet. This shall give a more inclusive perspective on the German language. We will argue that Swiss Standard German words, Helvetisms, are best included into the already existing wordnet GermaNet, rather than creating them as a separate wordnet.