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In two eye-tracking experiments, we investigated the relationship between the subject preference in the resolution of subject-object ambiguities in German embedded clauses and semantic word order constraints (i.e., prominence hierarchies relating to the specificity/referentiality of noun phrases, case assignment and thematic role assignment). Our central research question concerned the timecourse with which prominence information is used and particularly whether it modulates the subject preference. In both experiments, we replicated previous findings of reanalysis effects for object-initial structures. Our findings further suggest that noun phrase prominence does not alter initial parsing strategies (viz., the subject preference), but rather modulates the ease of later reanalysis processes. In Experiment 1, the object case assigned by the verb did not affect the ease of reanalysis. However, the syntactic reanalysis was rendered more difficult when the order of the two arguments violated the specificity/referentiality hierarchy. Experiment 2 revealed that the initial subject preference also holds for verbs favoring an object-initial base order (i.e., dative object-experiencer verbs). However, the advantage for subject-initial sentences is neutralized in relatively late processing stages when the thematic role hierarchy and the specificity hierarchy converge to promote scrambling.
Researchers interested in the sounds of speech or the physical gestures of Speakers make use of audio and video recordings in their work. Annotating these recordings presents a different set of requirements to the annotation of text. Special purpose tools have been developed to display video and audio Signals and to allow the creation of time-aligned annotations. This chapter reviews the most widely used of these tools for both manual and automatic generation of annotations on multimodal data.
We present a method to identify and document a phenomenon on which there is very little empirical data: German phrasal compounds occurring in the form of as a single token (without punctuation between their components). Relying on linguistic criteria, our approach implies to have an operational notion of compounds which can be systematically applied as well as (web) corpora which are large and diverse enough to contain rarely seen phenomena. The method is based on word segmentation and morphological analysis, it takes advantage of a data-driven learning process. Our results show that coarse-grained identification of phrasal compounds is best performed with empirical data, whereas fine-grained detection could be improved with a combination of rule-based and frequency-based word lists. Along with the characteristics of web texts, the orthographic realizations seem to be linked to the degree of expressivity.
We present an approach for modeling German negation in open-domain fine grained sentiment analysis. Unlike most previous work in sentiment analysis, we assume that negation can be conveyed by many lexical units (and not only common negation words) and that different negation words have different scopes. Our approach is examined on a new dataset comprising sentences with mentions of polar expressions and various negation words. We identify different types of negation words that have the same scopes. We show that already negation modeling based on these types largely outperforms traditional negation models which assume the same scope for all negation words and which employ a window-based scope detection rather than a scope detection based on syntactic information.
We present a method for detecting and reconstructing separated particle verbs in a corpus of spoken German by following an approach suggested for written language. Our study shows that the method can be applied successfully to spoken language, compares different ways of dealing with structures that are specific to spoken language corpora, analyses some remaining problems, and discusses ways of optimising precision or recall for the method. The outlook sketches some possibilities for further work in related areas.