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Using the Google Ngram Corpora for six different languages (including two varieties of English), a large-scale time series analysis is conducted. It is demonstrated that diachronic changes of the parameters of the Zipf–Mandelbrot law (and the parameter of the Zipf law, all estimated by maximum likelihood) can be used to quantify and visualize important aspects of linguistic change (as represented in the Google Ngram Corpora). The analysis also reveals that there are important cross-linguistic differences. It is argued that the Zipf–Mandelbrot parameters can be used as a first indicator of diachronic linguistic change, but more thorough analyses should make use of the full spectrum of different lexical, syntactical and stylometric measures to fully understand the factors that actually drive those changes.
Using the Google Ngram Corpora for six different languages (including two varieties of English), a large-scale time series analysis is conducted. It is demonstrated that diachronic changes of the parameters of the Zipf–Mandelbrot law (and the parameter of the Zipf law, all estimated by maximum likelihood) can be used to quantify and visualize important aspects of linguistic change (as represented in the Google Ngram Corpora). The analysis also reveals that there are important cross-linguistic differences. It is argued that the Zipf–Mandelbrot parameters can be used as a first indicator of diachronic linguistic change, but more thorough analyses should make use of the full spectrum of different lexical, syntactical and stylometric measures to fully understand the factors that actually drive those changes.
Compared with printed dictionaries, online dictionaries provide a number of unique possibilities for the presentation and processing of lexicographical information. However, in Müller-Spitzer/Koplenig/Töpel (2011) we show that – on average - users tend to rate the special characteristics of online dictionaries (e.g. multimedia, adaptability) as (partly) unimportant. This result conflicts somewhat with the lexicographical request both for the development of a user-adaptive interface and the incorporation of multimedia elements. This contribution seeks to explain this discrepancy, by arguing that when potential users are fully informed about the benefits of possible innovative features of online dictionaries, they will come to judge these characteristics to be more useful than users that do not have this kind of information. This argument is supported by empirical evidence presented in this paper.
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.
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.
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.
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.
As a result of legal restrictions the Google Ngram Corpora datasets are a) not accompanied by any metadata regarding the texts the corpora consist of and the data are b) truncated to prevent an indirect conclusion from the n-gram to the author of the text. Some of the consequences of this strategy are discussed in this article.
In a recent article, Meylan and Griffiths (Meylan & Griffiths, 2021, henceforth, M&G) focus their attention on the significant methodological challenges that can arise when using large-scale linguistic corpora. To this end, M&G revisit a well-known result of Piantadosi, Tily, and Gibson (2011, henceforth, PT&G) who argue that average information content is a better predictor of word length than word frequency. We applaud M&G who conducted a very important study that should be read by any researcher interested in working with large-scale corpora. The fact that M&G mostly failed to find clear evidence in favor of PT&G's main finding motivated us to test PT&G's idea on a subset of the largest archive of German language texts designed for linguistic research, the German Reference Corpus consisting of ∼43 billion words. We only find very little support for the primary data point reported by PT&G.