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In a previous study, Aceves and Evans present a large-scale quantitative information-theoretic analysis of parallel corpus data in ~1,000 languages to show that there are apparently strong associations between the way languages encode information into words and patterns of communication, e.g. the configuration of semantic information. During the peer review process, one reviewer raised the question of the extent to which the presented results depend on different corpus sizes (see the Peer Review File). This is a very important question given that most, if not all, of the quantities associated with word frequency distributions vary systematically with corpus size. While Aceves and Evans claim that corpus size does not affect the results presented, I challenge this view by presenting reanalyses of the data that clearly suggest that it does.
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.
Developments within the field of Second Language Acquisition (SLA) have meant that scholars are increasingly engaging with corpora and corpus-based resources, providing a source of “‘authentic’ language” to learners and educators (Mitchell 2020: 254), and contributing to “state-of-the-art research methodologies” (Deshors and Gries 2023: 164). However, there are areas in which progress can still be made, particularly in the area of metadata, such as information about the speaker and contexts of the language use, as well as increased variety in the text types and genres of corpora used to develop SLA materials (Paquot 2022: 36). This post discusses one such possibility for increasing the variety of text types and providing a rich source of authentic language that can be used to create engaging SLA materials, particularly for young people learning German, namely the use of the NottDeuYTSch corpus (to download the corpus in a variety of formats, see Cotgrove 2018).
We present a collection of (currently) about 5.500 commands directed to voice-controlled virtual assistants (VAs) by sixteen initial users of a VA system in their homes. The collection comprises recordings captured by the VA itself and with a conditional voice recorder (CVR) selectively capturing recordings including the VA-directed commands plus some surrounding context. Next to a description of the collection, we present initial findings on the patterns of use of the VA systems during the first weeks after installation, including usage timing, the development of usage frequency, distributions of sentence structures across commands, and (the development of) command success rates. We discuss the advantages and disadvantages of the applied collection-specific recording approach and describe potential research questions that can be investigated in the future, based on the collection, as well as the merit of combining quantitative corpus linguistic approaches with qualitative in-depth analyses of single cases.
This paper analyses intensification in German digitally-mediated communication (DMC) using a corpus of YouTube comments written by young people (the NottDeuYTSch corpus). Research on intensification in written language has traditionally focused on two grammatical aspects: syntactic intensification, i.e. the use of particles and other lexical items and morphological intensification, i.e. the use of compounding. Using a wide variety og examples from the corpus, the paper identifies novel ways that have been used for intensification in DMC, and suggests a new taxonomy of classification for future analysis of intensification.
This paper presents the IVK-Ler corpus, a longitudinal, annotated learner corpus of weekly writings produced by a group of 18 adolescents in a preparatory class. The corpus consists of 117 student texts collected between 2020 and 2021 and has a structure layered by student and text number. It includes metadata that enables researchers to analyze and track individual student progress in terms of syntactic competence and literacy. The annotation schema, manual and automatic annotation processes, and corpus representation are described in detail. The corpus currently includes target hypotheses and gold standard part-of-speech tags. Future work could include additional annotation layers for topological fields and dependency relations, as well as semantic and discourse annotations to make the corpus usable for tasks beyond syntactic evaluations.
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.
Speech islands are historically and developmentally unique and will inevitably disappear within the next decades. We urgently need to preserve their remains and exploit what is left in order to make research on language-in-contact and historical as well as current comparative language research possible.
The Archive for Spoken German (AGD) at the Institute for German Language collects, fosters and archives data from completed research projects and makes them available to the wider research community.
Besides large variation corpora and corpora of conversational speech, the archive already contains a range of collections of data on German speech minorities. The latter will be outlined in this chapter. Some speech island data is already made available through the personal service of the AGD, or the database of spoken German (DGD), e.g. data on Australian German, Unserdeutsch, or German in North America. Some corpora are still being prepared for publication, but still important to document for potentially interested research projects. We therefore also explain the current problems and efforts related to the curation of speech island data, from the digitization of recordings and the collection of metadata, to the integration of transcriptions, annotations and other ways of accessing and sharing data.
This paper presents an extended annotation and analysis of interpretative reply relations focusing on a comparison of reply relation types and targets between conflictual pages and neutral pages of German Wikipedia (WP) talk pages. We briefly present the different categories identified for interpretative reply relations to analyze the relationship between WP postings as well as linguistic cues for each category. We investigate referencing strategies of WP authors in discussion page postings, illustrated by means of reply relation types and targets taking into account the degree of disagreement displayed on a WP talk page. We provide richly annotated data that can be used for further analyses such as the identification of interactional relations on higher levels, or for training tasks in machine learning algorithms.