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The International Comparable Corpus (ICC) (Kirk/Čermáková 2017; Čermáková et al. 2021) is an open initiative which aims to improve the empirical basis for contrastive linguistics by compiling comparable corpora for many languages and making them as freely available as possible as well as providing tools with which they can easily be queried and analysed. In this contribution we present the first release of written language parts of the ICC which includes corpora for Chinese, Czech, English, German, Irish (partly), and Norwegian. Each of the released corpora contains 400k words distributed over 14 different text categories according to the ICC specifications. Our poster covers the design basics of the ICC, its TEI encoding, a demonstration of using the ICC via different query tools, and an outlook on future plans.
Similar to the European Reference Corpus EuReCo (Kupietz et al. 2020), ICC follows the approach of reusing existing linguistic resources wherever possible in order to cover as many languages as possible with realistic effort in as short a time as possible. In contrast to EuReCo, however, comparable corpus pairs are not defined dynamically in the usage phase, but the compositions of the corpora are fixed in the ICC design. The approaches are thus complementary in this respect. The design principles and composition of the ICC are based on those of the International Corpus of English (ICE) (Greenbaum (ed.) 1996), with the deviation that the ICC includes the additional text category blog post and excludes spoken legal texts (see Čermáková et al. 2021 for details). ICC’s fixed-design approach has the advantage that all single-language corpora in the ICC have the same composition with respect to the selected text types and that this guarantees that the selected broad spectrum of potential influencing variables for linguistic variation is always represented. The disadvantage, however, is that this can only be achieved for quite small corpora and that the generalisability of comparative findings based on the ICC corpora will often need to be checked on larger monolingual corpora or translation corpora (Čermáková/Ebeling/Oksefjell Ebeling forthcoming). Arguing that such issues with comparability and representativeness are inevitable, in one way or the other, and need to be dealt with, our poster will discuss and exemplify the text selections in more detail.
We introduce DeReKoGram, a novel frequency dataset containing lemma and part-of-speech (POS) information for 1-, 2-, and 3-grams from the German Reference Corpus. The dataset contains information based on a corpus of 43.2 billion tokens and is divided into 16 parts based on 16 corpus folds. We describe how the dataset was created and structured. By evaluating the distribution over the 16 folds, we show that it is possible to work with a subset of the folds in many use cases (e.g., to save computational resources). In a case study, we investigate the growth of vocabulary (as well as the number of hapax legomena) as an increasing number of folds are included in the analysis. We cross-combine this with the various cleaning stages of the dataset. We also give some guidance in the form of Python, R, and Stata markdown scripts on how to work with the resource.
This paper reports on recent developments within the European Reference Corpus EuReCo, an open initiative that aims at providing and using virtual and dynamically definable comparable corpora based on existing national, reference or other large corpora. Given the well-known shortcomings of other types of multilingual corpora such as parallel/translation corpora (shining-through effects, over-normalization, simplification, etc.) or web-based comparable corpora (covering only web material), EuReCo provides a unique linguistic resource offering new perspectives for fine-grained contrastive research on authentic cross-linguistic data, applications in translation studies and foreign language teaching and learning.
Dieser Beitrag beschreibt die Motivation und Ziele hinter der Initiative Europäisches Referenzkorpus EuReCo. Ausgehend von den Desiderata, die sich aufgrund der Defizite verfügbarer Forschungsdaten wie monolinguale Korpora, Parallelkorpora und Vergleichskorpora für den Sprachvergleich ergeben, werden die bisherigen und die laufenden Arbeiten im Rahmen von EuReCo präsentiert und anhand vergleichender deutsch-rumänischer Kookkurrenzanalysen neue Perspektiven für kontrastive Korpuslinguistik, die die EuReCo-Initiative öffnet, skizziert.
When comparing different tools in the field of natural language processing (NLP), the quality of their results usually has first priority. This is also true for tokenization. In the context of large and diverse corpora for linguistic research purposes, however, other criteria also play a role – not least sufficient speed to process the data in an acceptable amount of time. In this paper we evaluate several state of the art tokenization tools for German – including our own – with regard to theses criteria. We conclude that while not all tools are applicable in this setting, no compromises regarding quality need to be made.
When comparing different tools in the field of natural language processing (NLP), the quality of their results usually has first priority. This is also true for tokenization. In the context of large and diverse corpora for linguistic research purposes, however, other criteria also play a role – not least sufficient speed to process the data in an acceptable amount of time. In this paper we evaluate several state-ofthe-art tokenization tools for German – including our own – with regard to theses criteria. We conclude that while not all tools are applicable in this setting, no compromises regarding quality need to be made.
We present the use of count-based and predictive language models for exploring language use in the German Reference Corpus DeReKo. For collocation analysis along the syntagmatic axis we employ traditional association measures based on co-occurrence counts as well as predictive association measures derived from the output weights of skipgram word embeddings. For inspecting the semantic neighbourhood of words along the paradigmatic axis we visualize the high dimensional word embeddings in two dimensions using t-stochastic neighbourhood embeddings. Together, these visualizations provide a complementary, explorative approach to analysing very large corpora in addition to corpus querying. Moreover, we discuss count-based and predictive models w.r.t. scalability and maintainability in very large corpora.
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.
This paper reports on the efforts of twelve national teams in building the International Comparable Corpus (ICC; https://korpus.cz/icc) that will contain highly comparable datasets of spoken, written and electronic registers. The languages currently covered are Czech, Finnish, French, German, Irish, Italian, Norwegian, Polish, Slovak, Swedish and, more recently, Chinese, as well as English, which is considered to be the pivot language. The goal of the project is to provide much-needed data for contrastive corpus-based linguistics. The ICC corpus is committed to the idea of re-using existing multilingual resources as much as possible and the design is modelled, with various adjustments, on the International Corpus of English (ICE). As such, ICC will contain approximately the same balance of forty percent of written language and 60 percent of spoken language distributed across 27 different text types and contexts. A number of issues encountered by the project teams are discussed, ranging from copyright and data sustainability to technical advances in data distribution.