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This article examines the contrasts and commonalities between languages for specific purposes (LSP) and their popularizations on the one hand and the frequency patterns of LSP register features in English and German on the other. For this purpose corpora of expertexpert and expert-lay communication are annotated for part-of-speech and phrase structure information. On this basis, the frequencies of pre- and post-modifications in complex noun phrases are statistically investigated and compared for English and German. Moreover, using parallel and comparable corpora it is tested whether English-German translations obey the register norms of the target language or whether the LSP frequency patterns of the source language Ñshine throughì. The results provide an empirical insight into language contact phenomena involving specialized communication.
The author presents a study using eye-tracking-while-reading data from participants reading German jurisdictional texts. I am particularly interested in nominalisations. It can be shown that nominalisations are read significantly longer than other nouns and that this effect is quite strong. Furthermore, the results suggest that nouns are read faster in reformulated texts. In the reformulations, nominalisations were transformed into verbal structures. Reformulations did not lead to increased processing times of verbal constructions but reformulated texts were read faster overall. Where appropriate, results are compared to a previous study of Hansen et al. (2006) using the same texts but other methodology and statistical analysis.
Die öffentliche Akzeptanz und Wirkung natur- und technikwissenschaftlicher Forschung hängt grundlegend davon ab, ob sich die Ziele und Forschungsergebnisse an die Öffentlichkeit vermitteln lassen. Doch die Inhalte aktueller Forschungsvorhaben sind für ein Laienpublikum oft nur schwer zugänglich und verständlich. Vor dem Hintergrund, die gesellschaftliche Diskussion natur- und technikwissenschaftlicher Forschung zu verbessern, untersuchen und bewerten wir im Projekt PopSci – Understanding Science einen wichtigen Sektor des populärwissenschaftlichen Diskurses in Deutschland empirisch. Hierfür identifizieren wir die linguistischen Merkmale deutscher populärwissenschaftlicher Texte durch korpusbasierte Methoden und untersuchen deren Effekt auf die kognitive Verarbeitung der Texte durch Laien. Dazu setzen wir Vor- und Nachwissenstests ein. Außerdem messen wir die Blickbewegungen der Leserinnen und Leser, während sie populärwissenschaftliche Texte lesen. Aus dieser Kombination von unterschiedlichen Methoden versuchen wir, erste Empfehlungen zur Verbesserung des linguistischen Stils und der Wissensrepräsentation populärwissenschaftlicher Texte abzuleiten.
Reading corpora are text collections that are enriched with processing data. From a corpus linguist’s perspective, they can be seen as an extension of classical linguistic corpora with human language processing behavior. From a psycholinguist’s perspective, reading corpora allow to test psycholinguistic hypotheses on subsets of language and language processing as it is ‘in the wild’ – in contrast to strictly controlled language material in isolated sentences, as used in most psycholinguistic experiments. In this paper, we will investigate a relevance-based account of language processing which states that linguistic structures, that are embedded deeper syntactically, are read faster because readers allocate less attention to these structures.
Standardisierte statistische Auswertungen von Korpusdaten im Projekt "Korpusgrammatik" (KoGra-R)
(2017)
Wir zeigen anhand dreier Beispielanalysen, wie das im IDS-Projekt „Korpusgrammatik“ entwickelte Auswertungstool KoGra-R in der quantitativlinguistischen Forschung zur Analyse von Frequenzdaten auf mehreren linguistischen Ebenen eingesetzt werden kann. Wir demonstrieren dies anhand regionaler Präferenzen bei der Selektion von Genitivallomorphen, der Variation von Relativpronomina sowie der Verwendung bestimmter anaphorischer Ausdrucke in Abhängigkeit davon, ob sich das Antezedens im gleichen Satz befindet oder nicht. Die in KoGra-R implementierten statistischen Tests sind für jede dieser Ebenen geeignet, um mindestens einen ersten statistisch abgesicherten Eindruck der Datenlage zu erlangen.
In this contribution, we present a novel approach for the analysis of cross-reference structures in digital dictionaries on the basis of the complete dictionary database. Using paradigmatic items in the German Wiktionary as an example, we show how analyses based on graph theory can be fruitfully applied in this context, e. g. to gain an overview of paradigmatic references as a whole or to detect closely connected groups of headwords. Furthermore, we connect information about cross-reference structures with corpus frequencies and log file statistics. In this way, we can answer questions such as the following ones: Are frequent words paradigmatically linked more closely than others? Are closely linked headwords or headwords that stand more solitary in the dictionary visited significantly more often?
Wiktionary is increasingly gaining influence in a wide variety of linguistic fields such as NLP and lexicography, and has great potential to become a serious competitor for publisher-based and academic dictionaries. However, little is known about the "crowd" that is responsible for the content of Wiktionary. In this article, we want to shed some light on selected questions concerning large-scale cooperative work in online dictionaries. To this end, we use quantitative analyses of the complete edit history files of the English and German Wiktionary language editions. Concerning the distribution of revisions over users, we show that — compared to the overall user base — only very few authors are responsible for the vast majority of revisions in the two Wiktionary editions. In the next step, we compare this distribution to the distribution of revisions over all the articles. The articles are subsequently analysed in terms of rigour and diversity, typical revision patterns through time, and novelty (the time since the last revision). We close with an examination of the relationship between corpus frequencies of headwords in articles, the number of article visits, and the number of revisions made to articles.