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Nomen werden vermeintlich früher erworben als Verben, da sie dem Noun Bias zufolge konzeptuell einfacher sind. In Studien zum frühen Wortschatzerwerb spielen Verben folglich häufig keine prominente Rolle. Am Beispiel des Deutschen zeigt dieser Beitrag auf, wie sich die Verbbedeutung entwickelt. Dem hier vertretenen Ansatz des Event Structural Bootstrapping zufolge erschließen Kinder sich die Verbbedeutung durch eine Fokussierung auf den Endzustand. Daher spielen telische Verben fur den frühen Spracherwerb eine zentrale Rolle. Ergebnisse aus verschiedenen Spracherwerbsstudien zur Produktion und zur Interpretation von Verben bestätigen, dass deutschsprachige Kinder eine klare Endzustandsorientierung zeigen. Dass Verben und deren Semantik früher erworben werden als bis dato angenommen, spricht gleichzeitig gegen einen starken Noun Bias.
In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb “abandon” in “abandon all hope”. This is similar to how negation words like “not” can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available.
Based on the empirical data of 97 fourth-graders from three districts of Braunschweig in Germany, this paper investigates the possibility of changing semantic frames in multilingual communities. The focus of study is the verb field of self-motion. In a free-sorting task involving 52 verbs, Turkish-speaking students, in particular, placed the verbs schleichen (‘to sneak’) and kommen (‘to come’) in the same group. When explaining the perceived similarity they also used the word schleichen (‘to sneak’), in a specific grammatical construction that is not found in Standard German. This paper suggests that semantic frames may change along with grammatical constructions when typologically distinct languages come into close contact.
Complement phrases are essential for constructing well-formed sentences in German. Identifying verb complements and categorizing complement classes is challenging even for linguists who are specialized in the field of verb valency. Against this background, we introduce an ML-based algorithm which is able to identify and classify complement phrases of any German verb in any written sentence context. We use a large training set consisting of example sentences from a valency dictionary, enriched with POS tagging, and the ML-based technique of Conditional Random Fields (CRF) to generate the classification models.