This paper focuses on the automated extraction of argument components from user content in the German online participation project Tempelhofer Feld. We adapt existing argumentation models into a new model for decision-oriented online participation. Our model consists of three categories: major positions, claims, and premises. We create a new German corpus for argument mining by annotating our dataset with our model. Afterwards, we focus on the two classification tasks of identifying argumentative sentences and predicting argument components in sentences. We achieve macro-averaged F 1 measures of 69.77% and 68.5%, respectively.

Nowadays, there are a lot of natural language processing pipelines that are based on training data created by a few experts. This paper examines how the proliferation of the internet and its collaborative application possibilities can be practically used for NLP. For that purpose, we examine how the German version of Wiktionary can be used for a lemmatization task. We introduce IWNLP, an opensource parser for Wiktionary, that reimplements several MediaWiki markup language templates for conjugated verbs and declined adjectives. The lemmatization task is evaluated on three German corpora on which we compare our results with existing software for lemmatization. With Wiktionary as a resource, we obtain a high accuracy for the lemmatization of nouns and can even improve on the results of existing software for the lemmatization of nouns.

This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our un-supervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task's organizers.

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This paper describes our participation in the SemEval-2018 Task 12 Argument Reasoning Comprehension Task which calls to develop systems that, given a reason and a claim, predict the correct warrant from two opposing options. We decided to use a deep learning architecture and combined 623 models with different hyperparameters into an ensemble. Our extensive analysis of our architecture and ensemble reveals that the decision to use an ensemble was suboptimal. Additionally, we benchmark a support vector machine as a baseline. Furthermore, we experimented with an alternative data split and achieved more stable results.