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In this paper, a method for measuring synchronic corpus (dis-)similarity put forward by Kilgarriff (2001) is adapted and extended to identify trends and correlated changes in diachronic text data, using the Corpus of Historical American English (Davies 2010a) and the Google Ngram Corpora (Michel et al. 2010a). This paper shows that this fully data-driven method, which extracts word types that have undergone the most pronounced change in frequency in a given period of time, is computationally very cheap and that it allows interpretations of diachronic trends that are both intuitively plausible and motivated from the perspective of information theory. Furthermore, it demonstrates that the method is able to identify correlated linguistic changes and diachronic shifts that can be linked to historical events. Finally, it can help to improve diachronic POS tagging and complement existing NLP approaches. This indicates that the approach can facilitate an improved understanding of diachronic processes in language change.
Metalinguistic awareness of standard vs standard usage. The case of determiners in spoken German
(2015)
Online dictionary use
(2012)
This article presents empirical findings about what criteria make for a good online dictionary, using data on expectations and demands collected in an online questionnaire (N~684), complemented by additional results from a second questionnaire (N-390) which looked more closely at whether respondents had differentiated views on individual aspects of the criteria rated in the first study. Our results show that the classical criteria of reference books (such as reliability and clarity) were rated highest by our participants, whereas the unique characteristics of online dictionaries (such as multimedia and adaptability) were rated and ranked as (partly) unimportant. To verify whether or not the poor ratings of these innovative features were a result of the fact that our subjects are unfamiliar with online dictionaries incorporating such features, we incorporated an experiment into the second study. Our results revealed a learning effect: participants in the learning-effect condition, i.e. respondents who were first presented with examples of possible innovative features of online dictionaries, judged adaptability and multimedia to be more useful than participants who were not given that information. Thus, our data point to the conclusion that developing innovative features is worthwhile but that it should be borne in mind that users can only be persuaded of their benefits gradually. In addition, we present data about questions relating to the design of online dictionaries.
The Google Ngram Corpora seem to offer a unique opportunity to study linguistic and cultural change in quantitative terms. To avoid breaking any copyright laws, the data sets are not accompanied by any metadata regarding the texts the corpora consist of. Some of the consequences of this strategy are analyzed in this article. I chose the example of measuring censorship in Nazi Germany, which received widespread attention and was published in a paper that accompanied the release of the Google Ngram data (Michel et al. (2010): Quantitative analysis of culture using millions of digitized books. Science, 331(6014): 176–82). I show that without proper metadata, it is unclear whether the results actually reflect any kind of censorship at all. Collectively, the findings imply that observed changes in this period of time can only be linked directly to World War II to a certain extent. Therefore, instead of speaking about general linguistic or cultural change, it seems to be preferable to explicitly restrict the results to linguistic or cultural change ‘as it is represented in the Google Ngram data’. On a more general level, the analysis demonstrates the importance of metadata, the availability of which is not just a nice add-on, but a powerful source of information for the digital humanities.
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.
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.
Diachrone Wortschatzveränderungen werden in der Regel exemplarisch anhand bestimmter Phänomene oder Phänomenbereiche untersucht. Wir widmen uns der Frage, ob und wie Wandelprozesse auch auf globaler Ebene, also ohne sich auf bestimmte Wortschatzausschnitte festzulegen, messbar sind. Zur Untersuchung dieser Frage nutzen wir das Spiegel-Korpus, in dem alle Ausgaben der Wochenzeitschrift seit 1947 enthalten sind. Dabei gehen wir auf grundlegende Herausforderungen ein, die es dabei zu lösen gilt, wie die Verteilung sprachlicher Daten und die Folgen unterschiedlicher Subkorpusgrößen, d.h. im konkreten Fall die variierende Größe des Spiegelkorpus über die Zeit hinweg. Wir stellen ein Verfahren vor, mit dem wir in der Lage sind, flankiert von einem „Lackmustest“ zur Überprüfung der Ergebnisse, Wortschatzwandelprozesse bis auf die Mikroebene, d.h. zwischen zwei Monaten oder gar Wochen, quantitativ nachzuvollziehen.