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In many countries of the world, perspectives on gender equality and racism have changed in recent decades. One result has been more attention being devoted to traces of androcentric and racist language in society. This also affects dictionaries. In lexicography there are discussions about whether or to what extent social asymmetries are inscribed in dictionaries and if this is still acceptable. The issue of the nature of description plays an important role in this discussion. If sexist usages are often found in language use, i.e. in the corpus data on which the dictionary is based, does the dictionary also have to show them? How is this, in turn, compatible with the normative power of dictionaries? Do dictionaries contribute to the perpetuation of gender stereotypes by showcasing them under the banner of descriptive principles? And what roles do lexicographers play in this process? The article deals with these questions on the basis of individual lexicographical examples and current discussions in the lexicographic and public community.
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.
Der Anlass dieser Untersuchung war zunächst anekdotische Evidenz: Eines der Kinder der Autor*innen macht 2022 Abitur und las in ihrer gesamten gymnasialen Laufbahn genau eine ›Ganzschrift‹ einer Autorin: Die Judenbuche von Annette von Droste-Hülshoff. Zweifellos ein lesenswerter Text, aber konnte es wirklich sein, dass man in Deutschland 2022 Abitur macht, sogar Deutsch-Leistungskurs gewählt hat und sonst kein Buch einer Autorin im Deutschunterricht liest? Auch in den Pflichtlektüren für das Deutschabitur ist im entsprechenden Bundesland bei den empfohlenen Texten kein Roman und kein Drama einer Verfasserin verzeichnet. Neugierig geworden, recherchierten wir nach einer Liste, welche Literatur für den Deutschunterricht an Gymnasien in Baden-Württemberg (wo die Anekdote sich ereignete) insgesamt empfohlen wurde, und fanden auf den Seiten des Kultusministeriums eine umfangreiche Liste, auf der 298 Werke verzeichnet sind. Eine Auswertung nach dem Geschlecht der Verfasser*innen ergab, dass von den Einträgen auf dieser Liste 31 Titel bzw. Autor*innen (von) Frauen sind, d.h. rund 10 %.
Dictionaries are often a reflection of their time; their respective (socio-)historical context influences how the meaning of certain lexical units is described. This also applies to descriptions of personal terms such as man or woman. Lexicographers have a special responsibility to comprehensively investigate current language use before describing it in the dictionary. Accordingly, contemporary academic dictionaries are usually corpus-based. However, it is important to acknowledge that language is always embedded in cultural contexts. Our case study investigates differences in the linguistic contexts of the use of man and woman, drawing from a range of language collections (in our case fiction books, popular magazines and newspapers). We explain how potential differences in corpus construction would therefore influence the “reality” depicted in the dictionary. In doing so, we address the far-reaching consequences that the choice of corpus-linguistic basis for an empirical dictionary has on semantic descriptions in dictionary entries.Furthermore, we situate the case study within the context of gender-linguistic issues and discuss how lexicographic teams can engage with how dictionaries might perpetuate traditional role concepts when describing language use.
Dictionaries are often a reflection of their time; their respective (socio-)historical context influences how the meaning of certain lexical units is described. This also applies to descriptions of personal terms such as man or woman. Lexicographers have a special responsibility to comprehensively investigate current language use before describing it in the dictionary. Accordingly, contemporary academic dictionaries are usually corpus-based. However, it is important to acknowledge that language is always embedded in cultural contexts. Our case study investigates differences in the linguistic contexts of the use of man and woman, drawing from a range of language collections (in our case fiction books, popular magazines and newspapers). We explain how potential differences in corpus construction would therefore influence the “reality”1 depicted in the dictionary. In doing so, we address the far-reaching consequences that the choice of corpus-linguistic basis for an empirical dictionary has on semantic descriptions in dictionary entries.
Furthermore, we situate the case study within the context of gender-linguistic issues and discuss how lexicographic teams can engage with how dictionaries might perpetuate traditional role concepts when describing language use.
Dictionary usage research views dictionaries primarily as tools for solving linguistic problems. A large proportion of dictionary use now takes place online and can thus be easily monitored using tracking technologies. Using the data gathered through tracking usage data, we hope to optimize user experiences of dictionaries and other linguistic resources. Usage statistics are also used for external evaluation of linguistic resources. In this paper, we pursue the following three questions from a quantitative perspective: (1) What new insights can we gain from collecting and analysing usage data? (2) What limitations of the data and/or the collection process do we need to be aware of? (3) How can these insights and limitations inform the development and evaluation of linguistic resources?
Die Corona-Pandemie betrifft fast alle Facetten des öffentlichen Lebens und hat nicht nur erhebliche Auswirkungen auf den persönlichen Umgang miteinander, sondern beherrscht auch die Berichterstattung im großen Stil. In unserem Beitrag wollen wir zeigen, welche lexikalischen Spuren oder Trends der Coronakrise wir in der deutschen Online-Nachrichtenberichterstattung beobachten können, obwohl wir uns noch mitten in der Pandemie zu befinden scheinen. „Lexikalische Spuren“ bedeutet, dass wir z.B. die am häufigsten verwendeten Wörter, Wortbildungsprodukte rund um „Corona“ oder Häufigkeitskurven einzelner Wortformen analysieren. Auf der Grundlage von Online-Nachrichtenberichten aus 13 deutschsprachigen Quellen, die seit Anfang 2020 gesammelt wurden, zeigen wir unter anderem, wie über wöchentliche Übersichten der am häufigsten verwendeten Wörter zu sehen ist, wann die Corona-Pandemie zum dominierenden Thema in der Nachrichtenberichterstattung wird; wie eine wahre Explosion von Wortbildungsprodukten mit „Corona“ wie „Vor-Corona-Gesellschaft“ oder „Post-Corona Zukunft“ beobachtet werden kann, wie andere Themen – z.B. der Fußball – durch Corona verdrängt werden, wie sich die Diskussion um Auswege aus dem Lockdown in den Daten widerspiegelt, oder wie prominente Virolog/-innen in die gleiche „Frequenzliga“ wie Politiker/-innen aufsteigen.
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.