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What makes a good online dictionary? Empirical insights from an interdisciplinary research project
(2011)
This paper presents empirical fmdings from two online surveys on the use of online dictionaries, in which more than 1,000 participants took part. The aim of these studies was to clarify general questions of online dictionary use (e.g. which electronic devices are used for online dictionaries or different types of usage situations) and to identify different demands regarding the use of online dictionaries. We will present some important results ofthis ongoing research project by focusing on the latter. Our analyses show that neither knowledge of the participants’ (scientific or academic) background, nor the language Version of the online survey (German vs. English) allow any significant conclusions to be drawn about the participant’s individual user demands. Subgroup analyses only reveal noteworthy differences when the groups are clustered statistically. Taken together, our fmdings shed light on the general lexicographical request both for the development of a user-adaptive interface and the incorporation of multimedia elements to make online dictionaries more user-friendly and innovative.
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.
Online dictionary use
(2012)
We start by trying to answer a question that has already been asked by de Schryver et al. (2006): Do dictionary users (frequently) look up words that are frequent in a corpus. Contrary to their results, our results that are based on the analysis of log files from two different online dictionaries indicate that users indeed look up frequent words frequently. When combining frequency information from the Mannheim German Reference Corpus and information about the number of visits in the Digital Dictionary of the German Language as well as the German language edition of Wiktionary, a clear connection between corpus and look-up frequencies can be observed. In a follow-up study, we show that another important factor for the look-up frequency of a word is its temporal social relevance. To make this effect visible, we propose a de-trending method where we control both frequency effects and overall look-up trends.
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.
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)
Frimer et al. (2015) claim that there is a linear relationship between the level of prosocial language and the level of public disapproval of US Congress. A re-analysis demonstrates that this relationship is the result of a misspecified model that does not account for first-order autocorrelated disturbances. A Stata script to reproduce all presented results is available as an appendix.