L3: Lexik empirisch und digital
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In this article, we provide an insight into the development and application of a corpus-lexicographic tool for finding neologisms that are not yet listed in German dictionaries. As a starting point, we used the words listed in a glossary of German neologisms surrounding the COVID-19 pandemic. These words are lemma candidates for a new dictionary on COVID-19 discourse in German. They also provided the database used to develop and test the NeoRate tool. We report on the lexicographic work in our dictionary project, the design and functionalities of NeoRate, and describe the first test results with the tool, in particular with regard to previously unregistered words. Finally, we discuss further development of the tool and its possible applications.
The NottDeuYTSch corpus is a freely available collection of YouTube comments written under German-speaking videos by young people between 2008 and 2018. The article uses the NottDeuYTSch corpus to investigate how YouTube comments can be used to produce learning materials and how corpora of Digitally-Mediated Communication can benefit intermediate learners of German. The article details the effects of authentic communication within YouTube comments on teenage learners, examining how they can influence the psycholinguistic factors of motivation, foreign language anxiety, and willingness to communicate. The article also discusses the benefits and limitations of using authentic corpus material for the development of teaching material.
This paper introduces the Nottinghamer Korpus deutscher YouTube-Sprache (‘The Nottingham German YouTube Language Corpus’ - or NottDeuYTSch corpus). The corpus comprises over 33 million words, taken from roughly 3 million YouTube comments published between 2008 and 2018, written by a young, German-speaking demographic. The NottDeuYTSch corpus provides an authentic and representative linguistic snapshot of young German speakers and offers significant opportunities for in-depth research in several linguistic fields, such as lexis, morphology, syntax, orthography, multilingualism, and conversational and discursive analysis.
We introduce DeReKoGram, a novel frequency dataset containing lemma and part-of-speech (POS) information for 1-, 2-, and 3-grams from the German Reference Corpus. The dataset contains information based on a corpus of 43.2 billion tokens and is divided into 16 parts based on 16 corpus folds. We describe how the dataset was created and structured. By evaluating the distribution over the 16 folds, we show that it is possible to work with a subset of the folds in many use cases (e.g., to save computational resources). In a case study, we investigate the growth of vocabulary (as well as the number of hapax legomena) as an increasing number of folds are included in the analysis. We cross-combine this with the various cleaning stages of the dataset. We also give some guidance in the form of Python, R, and Stata markdown scripts on how to work with the resource.
Developments within the field of Second Language Acquisition (SLA) have meant that scholars are increasingly engaging with corpora and corpus-based resources, providing a source of “‘authentic’ language” to learners and educators (Mitchell 2020: 254), and contributing to “state-of-the-art research methodologies” (Deshors and Gries 2023: 164). However, there are areas in which progress can still be made, particularly in the area of metadata, such as information about the speaker and contexts of the language use, as well as increased variety in the text types and genres of corpora used to develop SLA materials (Paquot 2022: 36). This post discusses one such possibility for increasing the variety of text types and providing a rich source of authentic language that can be used to create engaging SLA materials, particularly for young people learning German, namely the use of the NottDeuYTSch corpus (to download the corpus in a variety of formats, see Cotgrove 2018).
This replication study aims to investigate a potential bias toward addition in the German language, building upon previous findings of Winter and colleagues who identified a similar bias in English. Our results confirm a bias in word frequencies and binomial expressions, aligning with these previous findings. However, the analysis of distributional semantics based on word vectors did not yield consistent results for German. Furthermore, our study emphasizes the crucial role of selecting appropriate translational equivalents, highlighting the significance of considering language-specific factors when testing for such biases for languages other than English.
The representative full-text digitalized HetWiK corpus is composed of 140 manually annotated texts of the German Resistance between 1933 and 1945. This includes both well-known and relatively unknown documents, public writings, like pamphlets or memoranda, as well as private texts, e.g. letters, journal or prison entries and biographies. Thus the corpus represents the diverse groups as well as the heterogeneity of verbal resistance and allows the study of resistance in relation to the language usage. The HetWiK corpus can be used free of charge. A detailed register of the individual texts and further information about the tagset can be found on the project-homepage (german). In addition to the CATMA5 XML-format we provide a standoff-JSON format and CEC6-Files (CorpusExplorer) - so you can export the HetWiK corpus in different formats.
Neologisms, i.e., new words or meanings, are finding their way into everyday language use all the time. In the process, already existing elements of a language are recombined or linguistic material from other languages is borrowed. But are borrowed neologisms accepted similarly well by the speech community as neologisms that were formed from “native” material? We investigate this question based on neologisms in German. Building on the corresponding results of a corpus study, we test the hypothesis of whether “native” neologisms are more readily accepted than those borrowed from English. To do so, we use a psycholinguistic experimental paradigm that allows us to estimate the degree of uncertainty of the participants based on the mouse trajectories of their responses. Unexpectedly, our results suggest that the neologisms borrowed from English are accepted more frequently, more quickly, and more easily than the “native” ones. These effects, however, are restricted to people born after 1980, the so-called millenials. We propose potential explanations for this mismatch between corpus results and experimental data and argue, among other things, for a reinterpretation of previous corpus studies.
Ziel dieses Projekts ist es, Sprachdaten so nah wie möglich am Jetzt zu erheben und analysierbar zu machen. Wir möchten, dass möglichst viele Menschen, nicht nur Sprachwissenschaftlerinnen und Sprachwissenschaftler, in die Lage versetzt werden, Sprachdaten zu explorieren und zu nutzen. Hierzu erheben wir ein Korpus, d. h. eine aufbereitete Sammlung von Sprachdaten von RSS-Feeds deutschsprachiger Onlinequellen. Wir zeichnen die Entwicklung der Analysewerkzeuge von einem Prototyp hin zur aktuellen Form der Anwendung nach, die eine komplette Reimplementierung darstellt. Dabei gehen wir auf die Architektur, einige Analysebeispiele sowie Erweiterungsmöglichkeiten ein. Fragen der Skalierbarkeit und Performanz stehen dabei im Mittelpunkt. Unsere Darstellungen lassen sich daher auf andere Data-Science-Projekte verallgemeinern.
This article details the process of creating the Nottinghamer Korpus deutscher YouTube-Sprache ('The Nottingham German YouTube Language Corpus' - or NottDeuYTSch corpus) and outlines potential research opportunities. The corpus was compiled to analyse the online language produced by young German-speakers and offers significant opportunity for in-depth research across several linguistic fields including lexis, morphology, syntax, orthography, and conversational and discursive analysis. The NottDeuYTSch corpus contains over 33 million words taken from approximately 3 million YouTube comments from videos published between 2008 to 2018 targeted at a young, German-speaking demographic and represent an authentic language snapshot of young German speakers. The corpus was proportionally sampled based on video category and year from a database of 112 popular German-speaking YouTube channels in the DACH region for optimal representativeness and balance and contains a considerable amount of associated metadata for each comment that enable further longitudinal cross-sectional analyses. The NottDeuYTSch corpus is available for analysis as part of the German Reference Corpus (DeReKo).