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One of the fundamental questions about human language is whether all languages are equally complex. Here, we approach this question from an information-theoretic perspective. We present a large scale quantitative cross-linguistic analysis of written language by training a language model on more than 6500 different documents as represented in 41 multilingual text collections consisting of ~ 3.5 billion words or ~ 9.0 billion characters and covering 2069 different languages that are spoken as a native language by more than 90% of the world population. We statistically infer the entropy of each language model as an index of what we call average prediction complexity. We compare complexity rankings across corpora and show that a language that tends to be more complex than another language in one corpus also tends to be more complex in another corpus. In addition, we show that speaker population size predicts entropy. We argue that both results constitute evidence against the equi-complexity hypothesis from an information-theoretic perspective.
This paper presents the results of a survey on dictionary use in Europe, the largest survey of dictionary use to date with nearly 10,000 participants in nearly thirty countries. The paper focuses on the comparison of the results of the Slovenian participants with the results of the participants from other European countries. The comparisons are made both with the European averages, and with the results from individual countries, in order to determine in which aspects Slovenian participants share similarities with other dictionary users (and non-users) around Europe, and in which aspects they differ. The findings show that in many ways the Slovenian users are similar to their European counterparts, with some noticeable exceptions, including (much) stronger preference for digital dictionaries over print ones, above-average reliance on other people when dictionary does not contain the relevant information, and the largest difference between the price of a dictionary and the amount willing to spend on it.
Dieser Beitrag gibt einen Überblick über die methodischen Ausgangspunkte des Projekts MIT. Qualität und stellt einige zentrale Erkenntnisse zur Modellbildung, der korpuslinguistischen Analyse und Akzeptabilitätserhebungen in der Sprachgemeinschaft vor. Wir zeigen dabei, wie bestehende Textqualitätsmodelle anhand einer Analyse einschlägiger Ratgeberliteratur erweitert werden können. Es wurden zwei empirische Fallstudien durchgeführt, die beide auf die Herstellung von textueller Kohärenz mittels des Kausalkonnektors weil fokussieren. Wir stellen zunächst eine korpuskontrastive Analyse vor. Weiterhin zeigen wir, wie man anhand verschiedener Aufgabenstellungen diverse Aspekte von Akzeptabilität in der Sprachgemeinschaft abprüfen kann.
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 %.
It was recently suggested in a study published in Nature Human Behaviour that the historical loosening of American culture was associated with a trade-off between higher creativity and lower order. To this end, Jackson et al. generate a linguistic index of cultural tightness based on the Google Books Ngram corpus and use this index to show that American norms loosened between 1800 and 2000. While we remain agnostic toward a potential loosening of American culture and a statistical association with creativity/order, we show here that the methods used by Jackson et al. are neither suitable for testing the validity of the index nor for establishing possible relationships with creativity/order.
This contribution explores the relationship between the English CEFR (Common European Framework of Reference for Languages) vocabulary levels and user interest in English Wiktionary entries. User interest was operationalized through the number of views of these entries in Wikimedia server logs covering a period of four years (2019–2022). Our findings reveal a significant relationship between CEFR levels and user interest: entries classified at lower CEFR levels tend to attract more views, which suggests a greater user interest in more basic vocabulary. A multiple regression model controlling for other known or potential factors affecting interest: corpus frequency, polysemy, word prevalence, and age of acquisition confirmed that lower CEFR levels attract significantly more views even after taking into account the other predictors. These findings highlight the importance of CEFR levels in predicting which words users are likely to look up, with implications for lexicography and the development of language learning materials.
Many studies on dictionary use presuppose that users do indeed consult lexicographic resources. However, little is known about what users actually do when they try to solve language problems on their own. We present an observation study where learners of German were allowed to browse the web freely while correcting erroneous German sentences. In this paper, we are focusing on the multi-methodological approach of the study, especially the interplay between quantitative and qualitative approaches. In one example study, we will show how the analysis of verbal protocols, the correction task and the screen recordings can reveal the effects of intuition, language (learning) awareness, and determination on the accuracy of the corrections. In another example study, we will show how preconceived hypotheses about the problem at hand might hinder participants from arriving at the correct solution.
In the past two decades, more and more dictionary usage studies have been published, but most of them deal with questions related to what users appreciate about dictionaries, which dictionaries they use and what type of information they need in specific situations — presupposing that users actually consult lexicographic resources. However, language teachers and lecturers in linguistics often have the impression that students do not use enough high-quality dictionaries in their everyday work. With this in mind, we launched an international cooperation project to collect empirical data to evaluate what it is that students actually do while attempting to solve language problems. To this end, we applied a new methodological setting: screen recording in conjunction with a thinking-aloud task. The collected empirical data offers a broad insight into what users really do while they attempt to solve language-related tasks online.
cOWIDplus
(2020)
Die Corona-Krise hat Einfluss auf die Sprache in deutschsprachigen Online-Medien. Wir haben die Hypothese, dass sich die Vielfältigkeit des verwendeten Vokabulars einschränkt. Wir glauben zudem, dass sich die Diversität des Vokabulars nach "überstandener" Krise wieder auf ein "Prä-Pandemie-Niveau" einpendeln wird. Diese zweite Hypothese lässt sich erst im Laufe der Zeit überprüfen.
cOWIDplus Analyse ist eine kontinuierlich aktualisierte Ressource zu der Frage, ob und wie stark sich der Wortschatz ausgewählter deutscher Online-Pressemeldungen während der Corona-Pandemie systematisch einschränkt und ob bzw. wann sich das Vokabular nach der Krise wieder ausweitet. In diesem Artikel erläutern die Autor*innen die hinter der Ressource stehende Forschungsfrage, die zugrunde gelegten Daten, die Methode sowie die bisherigen Ergebnisse.
cOWIDplus Viewer
(2020)
Eine europaweite Umfrage zu Wörterbuchbenutzung und -kultur. Ergebnisse der deutschen Teilnehmenden
(2018)
Gebrauchsgegenstand, Streitschlichter, Spielzeug, Nationalsymbol, Arbeitshilfe oder doch nur etwas, für das sich hauptsächlich Akademikerinnen und Akademiker interessieren? Welche Rolle spielen einsprachige Wörterbücher heute? Um unter anderen diesen Fragen nachzugehen, koordinierten wir gemeinsam mit Iztok Kosem (Universität Ljubljana) und Robert Lew (Adam-Mickiewicz Universität Poznań) die bis dato größte europaweite Umfrage zur Wörterbuchbenutzung und -kultur. Gemeinsam mit 26 ‚lokalen‘ Partnerinnen und Partnern aus ganz Europa führten wir im Rahmen des European Network of e-Lexicography (ENeL) diese Umfrage durch. Die Ergebnisse der Studie versprechen neue Einsichten in den gesellschaftlichen Status von Wörterbüchern in vielen europäischen Ländern. Durch die möglichst parallele Erhebung der Daten in den teilnehmenden Ländern werden außerdem interessante Vergleiche der lokalen ‚Wörterbuchkulturen‘ möglich sein. Im Fokus der Befragung standen allgemeine einsprachige Wörterbücher in der oder den jeweiligen Landessprache(n).
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.
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.
A central goal of linguistics is to understand the diverse ways in which human language can be organized (Gibson et al. 2019; Lupyan/Dale 2016). In our contribution, we present results of a large scale cross-linguistic analysis of the statistical structure of written language (Koplenig/Wolfer/Meyer 2023) we approach this question from an information-theoretic perspective. To this end, we conduct a large scale quantitative cross-linguistic analysis of written language by training a language model on more than 6,500 different documents as represented in 41 multilingual text collections, so-called corpora, consisting of ~3.5 billion words or ~9.0 billion characters and covering 2,069 different languages that are spoken as a native language by more than 90% of the world population. We statistically infer the entropy of each language model as an index of un. To this end, we have trained a language model on more than 6,500 different documents as represented in 41 parallel/multilingual corpora consisting of ~3.5 billion words or ~9.0 billion characters and covering 2,069 different languages that are spoken as a native language by more than 90% of the world population or ~46% of all languages that have a standardized written representation. Figure 1 shows that our database covers a large variety of different text types, e.g. religious texts, legalese texts, subtitles for various movies and talks, newspaper texts, web crawls, Wikipedia articles, or translated example sentences from a free collaborative online database. Furthermore, we use word frequency information from the Crúbadán project that aims at creating text corpora for a large number of (especially under-resourced) languages (Scannell 2007). We statistically infer the entropy rate of each language model as an information-theoretic index of (un)predictability/complexity (Schürmann/Grassberger 1996; Takahira/Tanaka-Ishii/Dębowski 2016). Equipped with this database and information-theoretic estimation framework, we first evaluate the so-called ‘equi-complexity hypothesis’, the idea that all languages are equally complex (Sampson 2009). We compare complexity rankings across corpora and show that a language that tends to be more complex than another language in one corpus also tends to be more complex in another corpus. This constitutes evidence against the equi-complexity hypothesis from an information-theoretic perspective. We then present, discuss and evaluate evidence for a complexity-efficiency trade-off that unexpectedly emerged when we analysed our database: high-entropy languages tend to need fewer symbols to encode messages and vice versa. Given that, from an information theoretic point of view, the message length quantifies efficiency – the shorter the encoded message the higher the efficiency (Gibson et al. 2019) – this indicates that human languages trade off efficiency against complexity. More explicitly, a higher average amount of choice/uncertainty per produced/received symbol is compensated by a shorter average message length. Finally, we present results that could point toward the idea that the absolute amount of information in parallel texts is invariant across different languages.
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.
Based on the privative derivational suffix -los, we test statements found in the literature on word formation using a – at least in this field – novel empirical basis: a list of affective-emotional ratings of base nouns and associated -los derivations. In addition to a frequency analysis based on the German Reference Corpus, we show that, in general, emotional polarity (so-called valence, positive vs. negative emotions) is reversed by suffixation with -los. This change is stronger for more polarized base nouns. The perceived intensity of emotion (so-called arousal) is generally lower for -los derivations than for base nouns. Finally, to capture the results theoretically, we propose a prototypical -los construction in the framework of Construction Morphology.
Computational language models (LMs), most notably exemplified by the widespread success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range of linguistic tasks, thus providing cognitive science and linguistics with a computational working model to empirically study different aspects of human language. Here, we use LMs to test the hypothesis that languages with more speakers tend to be easier to learn. In two experiments, we train several LMs—ranging from very simple n-gram models to state-of-the-art deep neural networks—on written cross-linguistic corpus data covering 1293 different languages and statistically estimate learning difficulty. Using a variety of quantitative methods and machine learning techniques to account for phylogenetic relatedness and geographical proximity of languages, we show that there is robust evidence for a relationship between learning difficulty and speaker population size. However, contrary to expectations derived from previous research, our results suggest that languages with more speakers tend to be harder to learn.
Less than one percent of words would be affected by gender-inclusive language in German press texts
(2024)
Research 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.
Im vorliegenden Beitrag gehen wir von der Prämisse aus, dass die Angemessenheit sprachlicher Formen nicht pauschal, sondern anhand des jeweiligen Kontexts zu beurteilen ist. Anhand einer Online-Fragebogenstudie mit durch weil eingeleiteten Nebensätzen untersuchen wir die Hypothese, dass Varianten, die nicht dem Schriftstandard entsprechen, in Kommunikationsformen, die sich weniger an standard- und schriftsprachlichen Normen orientieren, als (mindestens) ebenso angemessen oder zumindest unterschiedlich wahrgenommen werden wie eine schriftstandardsprachliche Variante. Wir untersuchen dies anhand von drei Aufgaben: Rezeption, Produktion und Assoziation zu bestimmten Medien und Textsorten. Wir können zeigen, dass die schriftnormgerechte Variante durchweg als am akzeptabelsten eingeschätzt wird. In allen drei Aufgaben finden sich aber auch eindeutige und übereinstimmende Effekte, die nahelegen, dass die verschiedenen Varianten in Abhängigkeit der Textsorte doch unterschiedlich eingeschätzt, produziert und assoziiert werden.
In der Geschichte der Sprachwissenschaft hat das Lexikon in unterschiedlichem Maße Aufmerksamkeit erfahren. In jüngerer Zeit ist es vor allem durch die Verfügbarkeit sprachlicher Massendaten und die Entwicklung von Methoden zu ihrer Analyse wieder stärker ins Zentrum des Interesses gerückt. Dies hat aber nicht nur unseren Blick für lexikalische Phänomene geschärft, sondern hat gegenwärtig auch einen profunden Einfluss auf die Entstehung neuer Sprachtheorien, beginnend bei Fragen nach der Natur lexikalischen Wissens bis hin zur Auflösung der Lexikon-Grammatik-Dichotomie. Das Institut für Deutsche Sprache hat diese Entwicklungen zum Anlass genommen, sein aktuelles Jahrbuch in Anknüpfung an die Jahrestagung 2017 – „Wortschätze: Dynamik, Muster, Komplexität“ – der Theorie des Lexikons und den Methoden seiner Erforschung zu widmen.
In a previous study published in Nature Human Behaviour, Varnum and Grossmann claim that reductions in gender inequality are linked to reductions in pathogen prevalence in the United States between 1951 and 2013. Since the statistical methods used by Varnum and Grossmann are known to induce (seemingly) significant correlations between unrelated time series, so-called spurious or non-sense correlations, we test here whether the statistical association between gender inequality and pathogens prevalence in its current form also is the result of mis-specified models that do not correctly account for the temporal structure of the data. Our analysis clearly suggests that this is the case. We then discuss and apply several standard approaches of modelling time-series processes in the data and show that there is, at least as of now, no support for a statistical association between gender inequality and pathogen prevalence.
We present studies using the 2013 log files from the German version of Wiktionary. We investigate several lexicographically relevant variables and their effect on look-up frequency: Corpus frequency of the headword seems to have a strong effect on the number of visits to a Wiktionary entry. We then consider the question of whether polysemic words are looked up more often than monosemic ones. Here, we also have to take into account that polysemic words are more frequent in most languages. Finally, we present a technique to investigate the time-course of look-up behaviour for specific entries. We exemplify the method by investigating influences of (temporary) social relevance of specific headwords.
Olaf Scholz gendert. Eine Analyse von Personenbezeichnungen in Weihnachts- und Neujahrsansprachen
(2022)
Schlagzeilen wie die in unserer Überschrift blieben im Januar 2022 aus. Dabei enthielt die erste Neujahrsansprache von Olaf Scholz kein einziges generisches Maskulinum, sondern Doppelformen (Mitbürgerinnen und Mitbürger, Expertinnen und Experten), geschlechtsabstrahierende Ausdrücke (Eltern, Familien, Geimpfte, Menschen) und Personalisierungen bzw. Umschreibungen wie uns allen, es haben sich 60 Millionen […] impfen lassen, oder ich möchte allen danken. Die Rede nutzt somit durchgängig verschiedene Formen geschlechtergerechter Sprache, wohl aber so unauffällige Formen, dass dies keine mediale Aufmerksamkeit auf sich gezogen hat. Nebenbei: Dies zeigt, dass es bei den hitzigen öffentlichen Diskussionen rund um das Thema nicht um alle Formen geschlechtergerechter Sprache geht, sondern eigentlich nur um bestimmte Formen, wie z.B. die Verwendung des Gendersterns. Wir stellen hier einige Beobachtungen basierend auf einem annotierten Korpus von Ansprachen vor, die Sie selbst anhand einer Online-App nachvollziehen können.
Dictionaries have been part and parcel of literate societies for many centuries. They assist in communication, particularly across different languages, to aid in understanding, creating, and translating texts. Communication problems arise whenever a native speaker of one language comes into contact with a speaker of another language. At the same time, English has established itself as a lingua franca of international communication. This marked tendency gives lexicography of English a particular significance, as English dictionaries are used intensively and extensively by huge numbers of people worldwide.
Quantitativ ausgerichtete empirische Linguistik hat in der Regel das Ziel, grose Mengen sprachlichen Materials auf einmal in den Blick zu nehmen und durch geeignete Analysemethoden sowohl neue Phanomene zu entdecken als auch bekannte Phanomene systematischer zu erforschen. Das Ziel unseres Beitrags ist es, anhand zweier exemplarischer Forschungsfragen methodisch zu reflektieren, wo der quantitativ-empirische Ansatz fur die Analyse lexikalischer Daten wirklich so funktioniert wie erhofft und wo vielleicht sogar systembedingte Grenzen liegen. Wir greifen zu diesem Zweck zwei sehr unterschiedliche Forschungsfragen heraus: zum einen die zeitnahe Analyse von produktiven Wortschatzwandelprozessen und zum anderen die Ausgleichsbeziehung von Wortstellungsvs. Wortstrukturregularitat in den Sprachen der Welt. Diese beiden Forschungsfragen liegen auf sehr unterschiedlichen Abstraktionsebenen. Wir hoffen aber, dass wir mit ihnen in groser Bandbreite zeigen konnen, auf welchen Ebenen die quantitative Analyse lexikalischer Daten stattfinden kann. Daruber hinaus mochten wir anhand dieser sehr unterschiedlichen Analysen die Moglichkeiten und Grenzen des quantitativen Ansatzes reflektieren und damit die Interpretationskraft der Verfahren verdeutlichen.
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.
Studying Lexical Dynamics and Language Change via Generalized Entropies: The Problem of Sample Size
(2019)
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.
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.
In an earlier publication it was claimed that there is no useful relationship between Swahili-English dictionary look-up frequencies and the occurrence frequencies for the same wordforms in Swahili-English corpora, at least not beyond the top few thousand wordforms. This result was challenged using data for German by a different team of researchers using an improved methodology. In the present article the original Swahili-English data is revisited, using ten years’ worth of it rather than just two, and using the improved methodology. We conclude that there is indeed a positive relationship. In addition, we show that online dictionary look-up behaviour is remarkably similar across languages, even when, as in our case, one is dealing with languages from very dissimilar language families. Furthermore, online dictionaries turn out to have minimum look-up success rates, below which they simply cannot go. These minima are language-sensitive and vary depending on the regularity of the searched-for entries, but are otherwise constant no matter the size of randomly sampled dictionaries. Corpus-informed sampling always improves on any random method. Lastly, from the point of view of the graphical user interface, we argue that the average user of an online bilingual dictionary is better served with a single search box, rather than separate search boxes for each dictionary side.
Languages employ different strategies to transmit structural and grammatical information. While, for example, grammatical dependency relationships in sentences are mainly conveyed by the ordering of the words for languages like Mandarin Chinese, or Vietnamese, the word ordering is much less restricted for languages such as Inupiatun or Quechua, as these languages (also) use the internal structure of words (e.g. inflectional morphology) to mark grammatical relationships in a sentence. Based on a quantitative analysis of more than 1,500 unique translations of different books of the Bible in almost 1,200 different languages that are spoken as a native language by approximately 6 billion people (more than 80% of the world population), we present large-scale evidence for a statistical trade-off between the amount of information conveyed by the ordering of words and the amount of information conveyed by internal word structure: languages that rely more strongly on word order information tend to rely less on word structure information and vice versa. Or put differently, if less information is carried within the word, more information has to be spread among words in order to communicate successfully. In addition, we find that–despite differences in the way information is expressed–there is also evidence for a trade-off between different books of the biblical canon that recurs with little variation across languages: the more informative the word order of the book, the less informative its word structure and vice versa. We argue that this might suggest that, on the one hand, languages encode information in very different (but efficient) ways. On the other hand, content-related and stylistic features are statistically encoded in very similar ways.
Am 24. Februar 2020 wurde in der Schweiz die erste Infektion mit dem Coronavirus nachgewiesen. Zu diesem Zeitpunkt konnte wohl noch niemand ahnen, welche tiefgreifenden Konsequenzen die Corona-Pandemie für die Gesellschaft haben wird. Aus heutiger Perspektive überrascht es uns nicht mehr, dass das Pandemiegeschehen auch starke Auswirkungen auf die Sprache hatte und noch immer hat, denn Sprachgebrauch passt sich stets gesellschaftlichen Veränderungen an. Am Leibniz-Institut für Deutsche Sprache in Mannheim dokumentieren und erforschen wir die ungewöhnlich starken und kurzfristigen Wirkungen der Pandemie auf die deutsche Sprache und fassen unsere Ergebnisse unter anderem in zahlreichen Beiträgen zusammen.
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.
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?
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.
Die ansprechende und geeignete Visualisierung linguistischer Daten gewinnt analog zum steigenden Einfluss quantitativer Methoden in der Linguistik immer mehr an Bedeutung. R ist eine flexible und freie Entwicklungsumgebung zur Umsetzung von statistischen Analysen, die zahlreiche Optionen zur Datenvisualisierung bereithält und sehr gut für große Datensätze geeignet ist. Statistische Analysen und Visualisierungen von Daten werden auf diese Weise in einer Umgebung verzahnt. Durch die zahlreichen Zusatzpakete stehen auch weiterhin zeitgemäße Methoden zur Verfügung, um (linguistische) Daten zu analysieren und darzustellen.
Der Beitrag vermittelt einen stark anwendungsorientierten Einstieg in das Programm und legt mithilfe von vielen praktischen Übungen und Anwendungsbeispielen die Grundlagen für ein eigenständiges Weiterentwickeln der individuellen Fähigkeiten im Umgang mit der Software. Neben einer kurzen, eher theoretisch angelegten Einleitung zu explorativen und explanatorischen Visualisierungsstrategien von Daten werden verschiedene Pakete vorgestellt, die für die Visualisierung in R benutzt werden können.
We present ESDexplorer (https://owid.shinyapps.io/ESDexplorer), a browser application which allows the user to explore the data from a large European survey on dictionary use and culture. We built ESDexplorer with several target groups in mind: our cooperation partners, other researchers, and a more general public interested in the results. Also, we present in detail the architecture and technological realisation of the application and discuss some legal aspects of data protection that motivated some architectural choices.
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.