Quantitative Linguistik
Refine
Year of publication
Document Type
- Article (13)
- Part of a Book (4)
- Other (2)
- Doctoral Thesis (1)
Is part of the Bibliography
- yes (20)
Keywords
- Sprachstatistik (9)
- Wortschatz (9)
- COVID-19 (8)
- Deutsch (8)
- Lexikostatistik (8)
- Online-Medien (8)
- Vielfalt (8)
- Korpus <Linguistik> (5)
- Entropie (3)
- Sprachwandel (3)
Publicationstate
- Veröffentlichungsversion (14)
- Zweitveröffentlichung (6)
- Postprint (3)
Reviewstate
Publisher
- Leibniz-Institut für Deutsche Sprache (IDS) (7)
- De Gruyter (2)
- MDPI (2)
- Springer Nature (2)
- Benjamins (1)
- IDS-Verlag (1)
- Royal Society of London (1)
- Stata Press (1)
- Universität Mannheim (1)
- Wilhelm Fink (1)
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.
Der folgende Leitfaden bietet eine grundlegende Übersicht darüber, welche Schritte bei der Konzeption und Durchführung einer empirischen Untersuchung in der germanistischen Linguistik zu beachten sind. Wir werden den grundlegenden Ablauf und die zugrunde liegenden Konzepte allgemein bzw. modellhaft beschreiben und sie anhand von einfachen Beispielen illustrieren. Eine stärkere Ausgestaltung anhand von Beispielen zu verschiedenen linguistischen Forschungsfragen und -feldern und damit auch mehr Illustrationen, wie die einzelnen Schritte für bestimmte Forschungsfragen umzusetzen sind, finden Sie in den Fallstudien im —> Teil III dieses Bandes. Detailliertere Ausführungen zu den zentralen Konzepten des empirischen Arbeitens in der Linguistik finden Sie in —> Teil VI dieses Bandes. Weiterführende Literatur findet sich am Ende des Beitrags.
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.
Information theory can be used to assess how efficiently a message is transmitted on the basis of different symbolic systems. In this paper, I estimate the information-theoretic efficiency of written language for parallel text data in more than 1000 different languages, both on the level of characters and on the level of words as information encoding units. The main results show that (i) the median efficiency is ∼29% on the character level and ∼45% on the word level, (ii) efficiency on both levels is strongly correlated with each other and (iii) efficiency tends to be higher for languages with more speakers.
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.