Suchergebnisse

This study aims to establish what lexical factors make it more likely for dictionary users to consult specific articles in a dictionary using the English Wiktionary log files, which include records of user visits over the course of 6 years. Recent findings suggest that lexical frequency is a significant factor predicting look-up behavior, with the more frequent words being more likely to be consulted. Three further lexical factors are brought into focus: (1) age of acquisition; (2) lexical prevalence; and (3) degree of polysemy operationalized as the number of dictionary senses. Age of acquisition and lexical prevalence data were obtained from recent published studies and linked to the list of visited Wiktionary lemmas, whereas polysemy status was derived from Wiktionary entries themselves. Regression modeling confirms the significance of corpus frequency in explaining user interest in looking up words in the dictionary. However, the remaining three factors also make a contribution whose nature is discussed and interpreted. Knowing what makes dictionary users look up words is both theoretically interesting and practically useful to lexicographers, telling them which lexical items should be prioritized in lexicographic work.

AbstractTraditionally, language learners have relied on dictionaries when trying to read or write in a foreign language. However, new LLM-based chatbots may offer an alternative to traditional dictionaries as lexical tools. This study assesses the effectiveness of ChatGPT versus the monolingual Longman Dictionary of Contemporary English (LDOCE, 2024) and the bilingual Diki.pl (Diki.pl, 2024) online dictionaries in supporting English language learners in receptive and productive lexical tasks. With a sample of 166 university students at B2 to C1 proficiency levels and forty uncommon English phrasal verbs, we investigate whether a leading AI-driven chatbot, a high-quality learners' dictionary, or a popular free bilingual dictionary offers better support in accurately understanding and producing English. The results reveal ChatGPT to be more effective than either dictionary in production, and better than the monolingual dictionary, but not the bilingual dictionary, in reception.

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 affects almost every facet of public life and not only has a major impact in personal interaction, but also dominates the news on a large scale. In our article we aim to show which lexical traces or trends of the coronavirus crisis we can already observe in German online news reports, although we still seem to be right in the middle of the pandemic. Lexical traces mean that we investigate topics related to the coronavirus pandemic by analyzing e.g. the most frequently used words, word formation products around corona or the frequency curves of single word forms. Based on online news reports from 13 German language sources collected since the beginning of 2020, we ...

AbstractResearch on gender and language is tightly knitted to social debates on gender equality and non-discriminatory language use. Psycholinguistic scholars have made significant contributions in this field. However, corpus-based studies that investigate these matters within the context of language use are still rare. In our study, we address the question of how much textual material would actually have to be changed if non-gender-inclusive texts were rewritten to be gender-inclusive. This quantitative measure is an important empirical insight, as a recurring argument against the use of gender-inclusive German is that it supposedly makes written texts too long and complicated. It is also argued that gender-inclusive language has negative effects on language learners. However, such effects are only likely if gender-inclusive texts are very different from those that are not gender-inclusive. In our corpus-linguistic study, we manually annotated German press texts to identify the parts that would have to be changed. Our results show that, on average, less than 1% of all tokens would be affected by gender-inclusive language. This small proportion calls into question whether gender-inclusive German presents a substantial barrier to understanding and learning the language, particularly when we take into account the potential complexities of interpreting masculine generics.