Refine
Year of publication
- 2020 (3) (remove)
Document Type
- Article (2)
- Part of a Book (1)
Language
- English (3) (remove)
Has Fulltext
- yes (3)
Is part of the Bibliography
- yes (3)
Keywords
- Deutsch (2)
- COVID-19 (1)
- Empirische Linguistik (1)
- Entropie (1)
- German (1)
- Jensen-Shannon divergence (1)
- Lexikostatistik (1)
- Neologismus (1)
- Online-Medien (1)
- RSS newsfeed corpus (1)
Publicationstate
- Postprint (2)
- Zweitveröffentlichung (2)
- Veröffentlichungsversion (1)
Reviewstate
- Peer-Review (3)
Publisher
- Benjamins (1)
- MDPI (1)
- Oxford University Press (1)
The coronavirus pandemic may be the largest crisis the world has had to face since World War II. It does not come as a surprise that it is also having an impact on language as our primary communication tool. In this short paper, we present three inter-connected resources that are designed to capture and illustrate these effects on a subset of the German language: An RSS corpus of German-language newsfeeds (with freely available untruncated frequency lists), a continuously updated HTML page tracking the diversity of the vocabulary in the RSS corpus and a Shiny web application that enables other researchers and the broader public to explore the corpus in terms of basic frequencies.
Studying Lexical Dynamics and Language Change via Generalized Entropies: The Problem of Sample Size
(2020)
Recently, it was demonstrated that generalized entropies of order α offer novel and important opportunities to quantify the similarity of symbol sequences where α is a free parameter. Varying this parameter makes it possible to magnify differences between different texts at specific scales of the corresponding word frequency spectrum. For the analysis of the statistical properties of natural languages, this is especially interesting, because textual data are characterized by Zipf’s law, i.e., there are very few word types that occur very often (e.g., function words expressing grammatical relationships) and many word types with a very low frequency (e.g., content words carrying most of the meaning of a sentence). Here, this approach is systematically and empirically studied by analyzing the lexical dynamics of the German weekly news magazine Der Spiegel (consisting of approximately 365,000 articles and 237,000,000 words that were published between 1947 and 2017). We show that, analogous to most other measures in quantitative linguistics, similarity measures based on generalized entropies depend heavily on the sample size (i.e., text length). We argue that this makes it difficult to quantify lexical dynamics and language change and show that standard sampling approaches do not solve this problem. We discuss the consequences of the results for the statistical analysis of languages.
Are borrowed neologisms accepted more slowly into the German language than German words resulting from the application of word formation rules? This study addresses this question by focusing on two possible indicators for the acceptance of neologisms: a) frequency development of 239 German neologisms from the 1990s (loanwords as well as new words resulting from the application of word formation rules) in the German reference corpus DeReKo and b) frequency development in the use of pragmatic markers (‘flags’, namely quotation marks and phrases such as sogenannt ‘so-called’) with these words. In the second part of the article, a psycholinguistic approach to evaluating the (psychological) status of different neologisms and non-words in an experimentally controlled study and plans to carry out interviews in a field test to collect speakers’ opinions on the acceptance of the analysed neologisms are outlined. Finally, implications for the lexicographic treatment of both types of neologisms are discussed.