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In this paper, the authors use the 2012 log files of two German online dictionaries (Digital Dictionary of the German Language and the German Version of Wiktionary) and the 100,000 most frequent words in the Mannheim German Reference Corpus from 2009 to answer the question of whether dictionary users really do look up frequent words, first asked by de Schryver et al. (2006). By using an approach to the comparison of log files and corpus data which is completely different from that of the aforementioned authors, we provide empirical evidence that indicates - contrary to the results of de Schryver et al. and Verlinde/Binon (2010) - that the corpus frequency of a word can indeed be an important factor in determining what online dictionary users look up. Finally, we incorporate word class Information readily available in Wiktionary into our analysis to improve our results considerably.
We present the conceptual foundations and basic features of fLexiCoGraph, a generic software package for creating and presenting curated human-oriented lexicographical resources that are roughly modeled according to Měchura’s (2016) idea of graph-augmented trees. The system is currently under development and will be made accessible as open source software. As a sample use case we discuss an existing online database of loanwords borrowed from German into other languages which is based on a growing number of language-specific loanword dictionaries (Lehnwortportal Deutsch). The paper outlines the conceptual foundations of fLexiCoGraph’s hybrid graph/XML data model. To establish a database, XML-based resources may be imported or even input manually. An additional graph database layer is then constructed from these XML source documents in a freely configurable, but automated way; subsequently, the resulting graph can be manipulated and enlarged through a visual user interface in such a way that keeps the relationship to the source document information explicit at all times. We sketch the tooling support for different kinds of graph-level editing processes, including mechanisms for dealing with updated XML source documents and coping with duplicate or inconsistent information, and briefly discuss the browser interface for end users.
A central goal of linguistics is to understand the diverse ways in which human language can be organized (Gibson et al. 2019; Lupyan/Dale 2016). In our contribution, we present results of a large scale cross-linguistic analysis of the statistical structure of written language (Koplenig/Wolfer/Meyer 2023) we approach this question from an information-theoretic perspective. To this end, we conduct a large scale quantitative cross-linguistic analysis of written language by training a language model on more than 6,500 different documents as represented in 41 multilingual text collections, so-called corpora, consisting of ~3.5 billion words or ~9.0 billion characters and covering 2,069 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 un. To this end, we have trained a language model on more than 6,500 different documents as represented in 41 parallel/multilingual corpora consisting of ~3.5 billion words or ~9.0 billion characters and covering 2,069 different languages that are spoken as a native language by more than 90% of the world population or ~46% of all languages that have a standardized written representation. Figure 1 shows that our database covers a large variety of different text types, e.g. religious texts, legalese texts, subtitles for various movies and talks, newspaper texts, web crawls, Wikipedia articles, or translated example sentences from a free collaborative online database. Furthermore, we use word frequency information from the Crúbadán project that aims at creating text corpora for a large number of (especially under-resourced) languages (Scannell 2007). We statistically infer the entropy rate of each language model as an information-theoretic index of (un)predictability/complexity (Schürmann/Grassberger 1996; Takahira/Tanaka-Ishii/Dębowski 2016). Equipped with this database and information-theoretic estimation framework, we first evaluate the so-called ‘equi-complexity hypothesis’, the idea that all languages are equally complex (Sampson 2009). 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. This constitutes evidence against the equi-complexity hypothesis from an information-theoretic perspective. We then present, discuss and evaluate evidence for a complexity-efficiency trade-off that unexpectedly emerged when we analysed our database: high-entropy languages tend to need fewer symbols to encode messages and vice versa. Given that, from an information theoretic point of view, the message length quantifies efficiency – the shorter the encoded message the higher the efficiency (Gibson et al. 2019) – this indicates that human languages trade off efficiency against complexity. More explicitly, a higher average amount of choice/uncertainty per produced/received symbol is compensated by a shorter average message length. Finally, we present results that could point toward the idea that the absolute amount of information in parallel texts is invariant across different languages.
This paper presents the Lehnwortportal Deutsch, a new, freely accessible publication platform for resources on German lexical borrowings in other languages, to be launched in the second half of 2022. The system will host digital-native sources as well as existing, digitized paper dictionaries on loanwords, initially for some 15 recipient languages. All resources remain accessible as individual standalone dictionaries; in addition, data on words (etyma, loanwords etc.) together with their senses and relations to each other is represented as a cross-resource network in a graph database, with careful distinction between information present in the original sources and the curated portal network data resulting from matching and merging information on, e. g., lexical units appearing in multiple dictionaries. Special tooling is available for manually creating graphs from dictionary entries during digitization and for editing and augmenting the graph database. The user interface allows users to browse individual dictionaries, navigate through the underlying graph and ‘click together’ complex queries on borrowing constellations in the graph in an intuitive way. The web application will be available as open source.
Physicists look at language
(2006)