The 20 most recently published documents
Politische Grenzen haben nachweislich sowohl auf den Sprachgebrauch als auch auf die Sprachwahrnehmung einen großen Einfluss. Die vorliegende Arbeit analysiert für den die Länder Deutschland, Österreich und Italien übergreifenden bairischen Sprachraum, wie Sprecher/Hörer diesen räumlich (horizontal-areal) sowie hinsichtlich seines Verhaltensspektrums (vertikal-sozial) gliedern. Dabei werden die Wahrnehmungen sprachlicher und außersprachlicher Merkmale und die Einstellungen dazu genauer betrachtet.
Mithilfe eines pluridimensionalen Erhebungssettings, bestehend aus Tiefeninterview, Online-Fragebogen, Mental-Map-Erhebung und Hörerurteilstest, kann gezeigt werden, dass extralinguistische Barrieren, wie etwa politische Grenzen, stark mit attitudinal-perzeptiven Grenzen korrelieren. Damit stellt im Bewusstsein der Befragten auch die Staatsgrenze zwischen Deutschland und Österreich eine Sprachgrenze dar.
This paper shows that the phenomenon of plesionymy deserves greater attention and needs to be approached outside its traditional framework, which considered it to be a subtype of synonymy (Cruse, 1986, 2002; Croft and Cruse, 2004). This view suggested that pairs of terms such as foggy–misty, fearless–brave exhibit significant shared semantic traits that are more salient than their differences. Differing properties were considered to be subordinate. These are sometimes contextually foregrounded resulting in occasional oppositeness. Corpus studies show that this view is a broad generalization. This study sheds new light on German plesionyms by employing a corpus-linguistic approach. In particular, terms designating gradable properties (e.g. kritisch–ernst ‘critical–serious’, sauber–rein ‘clean–unsoiled/immaculate’) at neighboring positions of gradable scales show variable behavior and do not show a stronger affinity for synonymy. The position taken is that a relation of synonymy and contrast are equally a matter of construal. Both types of semantic relations are part of the conceptual and lexical knowledge and subject to a cognitive principle. This work also examines how plesionym relations are realized in discourse. This article demonstrates that plesionyms are co-occurrences within typical lexico-syntactic sequences. Following Jones’ (2002) and Murphy’s (2006) observations, these patterns (e.g. nicht X, eher Y; mehr X als Y; etc.) have specific discourse functions and are evidence to account for a construction-based view.
Conventional descriptions of synonymous items often concentrate on common semantic traits and the degree of semantic overlap they exhibit. Their aim is to offer classifications of synonymy rather than elucidating ways of establishing contextual meaning equivalence and the cognitive prerequisites for this. Generally, they lack explanations as to how synonymy is construed in actual language use. This paper investigates principles and cognitive devices of synonymy construction as they appear in corpus data, and focuses on questions of how meaning equivalence might be conceptualised by speakers.
Corpora with high-quality linguistic annotations are an essential component in many NLP applications and a valuable resource for linguistic research. For obtaining these annotations, a large amount of manual effort is needed, making the creation of these resources time-consuming and costly. One attempt to speed up the annotation process is to use supervised machine-learning systems to automatically assign (possibly erroneous) labels to the data and ask human annotators to correct them where necessary. However, it is not clear to what extent these automatic pre-annotations are successful in reducing human annotation effort, and what impact they have on the quality of the resulting resource. In this article, we present the results of an experiment in which we assess the usefulness of partial semi-automatic annotation for frame labeling. We investigate the impact of automatic pre-annotation of differing quality on annotation time, consistency and accuracy. While we found no conclusive evidence that it can speed up human annotation, we found that automatic pre-annotation does increase its overall quality.