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We investigate the optional omission of the infinitival marker in a Swedish future tense construction. During the last two decades the frequency of omission has been rapidly increasing, and this process has received considerable attention in the literature. We test whether the knowledge which has been accumulated can yield accurate predictions of language variation and change. We extracted all occurrences of the construction from a very large collection of corpora. The dataset was automatically annotated with language-internal predictors which have previously been shown or hypothesized to affect the variation. We trained several models in order to make two kinds of predictions: whether the marker will be omitted in a specific utterance and how large the proportion of omissions will be for a given time period. For most of the approaches we tried, we were not able to achieve a better-than-baseline performance. The only exception was predicting the proportion of omissions using autoregressive integrated moving average models for one-step-ahead forecast, and in this case time was the only predictor that mattered. Our data suggest that most of the language-internal predictors do have some effect on the variation, but the effect is not strong enough to yield reliable predictions.
In this paper, we present the concept and the results of two studies addressing (potential) users of monolingual German online dictionaries, such as www.elexiko.de. Drawing on the example of elexiko, the aim of those studies was to collect empirical data on possible extensions of the content of monolingual online dictionaries, e.g. the search function, to evaluate how users comprehend the terminology of the user interface, to find out which types of information are expected to be included in each specific lexicographic module and to investigate general questions regarding the function and reception of examples illustrating the use of a word. The design and distribution of the surveys is comparable to the studies described in the chapters 5-8 of this volume. We also explain, how the data obtained in our studies were used for further improvement of the elexiko-dictionary.
In the first volume of Corpus Linguistics and Linguistic Theory, Gries (2005. Null-hypothesis significance testing of word frequencies: A follow-up on Kilgarriff. Corpus Linguistics and Linguistic Theory 1(2). doi:10.1515/ cllt.2005.1.2.277. http://www.degruyter.com/view/j/cllt.2005.1.issue-2/cllt.2005. 1.2.277/cllt.2005.1.2.277.xml: 285) asked whether corpus linguists should abandon null-hypothesis significance testing. In this paper, I want to revive this discussion by defending the argument that the assumptions that allow inferences about a given population – in this case about the studied languages – based on results observed in a sample – in this case a collection of naturally occurring language data – are not fulfilled. As a consequence, corpus linguists should indeed abandon null-hypothesis significance testing.
Classical null hypothesis significance tests are not appropriate in corpus linguistics, because the randomness assumption underlying these testing procedures is not fulfilled. Nevertheless, there are numerous scenarios where it would be beneficial to have some kind of test in order to judge the relevance of a result (e.g. a difference between two corpora) by answering the question whether the attribute of interest is pronounced enough to warrant the conclusion that it is substantial and not due to chance. In this paper, I outline such a test.
This thesis consists of the following three papers that all have been published in international peer-reviewed journals:
Chapter 3: Koplenig, Alexander (2015c). The Impact of Lacking Metadata for the Measurement of Cultural and Linguistic Change Using the Google Ngram Data Sets—Reconstructing the Composition of the German Corpus in Times of WWII. Published in: Digital Scholarship in the Humanities. Oxford: Oxford University Press. [doi:10.1093/llc/fqv037]
Chapter 4: Koplenig, Alexander (2015b). Why the quantitative analysis of dia-chronic corpora that does not consider the temporal aspect of time-series can lead to wrong conclusions. Published in: Digital Scholarship in the Humanities. Oxford: Oxford University Press. [doi:10.1093/llc/fqv030]
Chapter 5: Koplenig, Alexander (2015a). Using the parameters of the Zipf–Mandelbrot law to measure diachronic lexical, syntactical and stylistic changes – a large-scale corpus analysis. Published in: Corpus Linguistics and Linguistic Theory. Berlin/Boston: de Gruyter. [doi:10.1515/cllt-2014-0049]
Chapter 1 introduces the topic by describing and discussing several basic concepts relevant to the statistical analysis of corpus linguistic data. Chapter 2 presents a method to analyze diachronic corpus data and a summary of the three publications. Chapters 3 to 5 each represent one of the three publications. All papers are printed in this thesis with the permission of the publishers.
In a previous study, Aceves and Evans present a large-scale quantitative information-theoretic analysis of parallel corpus data in ~1,000 languages to show that there are apparently strong associations between the way languages encode information into words and patterns of communication, e.g. the configuration of semantic information. During the peer review process, one reviewer raised the question of the extent to which the presented results depend on different corpus sizes (see the Peer Review File). This is a very important question given that most, if not all, of the quantities associated with word frequency distributions vary systematically with corpus size. While Aceves and Evans claim that corpus size does not affect the results presented, I challenge this view by presenting reanalyses of the data that clearly suggest that it does.
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.
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.
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.
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.