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"Reproducibility crisis" and "empirical turn" are only two keywords when it comes to providing reasons for research data management. Research data is omnipresent and with the more and more automatic data processing procedures, they become even more important. However, just because new methods require data and produce data, this does not mean that data are easily accessible, reusable or even make a difference in the CV of a researcher, even if a large portion of research goes into data creation, acquisition, preparation, and analysis. In this talk I will present where we find data in the research process, where we may find appropriate support for data management and advocate for a procedure for including it in research publications and resumes.
This presentation relies on work within the BMBF-funded project CLARIN-D. It also builds on work within the German National Research Data Infrastructure (NFDI) consortium Text+, DFG project number 460033370.
Prediction is a central mechanism in the human language processing architecture. The psycholinguistic and neurolinguistic literature has seen a lively debate about what form prediction may take and what status it has for language processing in the human mind and brain. While predictions are a ubiquitous finding, the implications of these results for models of language processing differ. For instance, eyetracking data suggest that predictions may rely on sublexical orthographic information in natural reading, while electrophysiological data provide mixed evidence for form-based predictions during reading. Other research has revealed that humans rapidly adapt to text specifics and that their predictive capacity varies, broadly speaking, in accordance with inter- and intra-individual language proficiency, which cuts across the speaker groups (e.g. L1 vs. L2 speakers, skilled vs. untrained readers) traditionally used for experimental contrasts. There is therefore evidence that the kind and strength of linguistic predictions depend on (at least) three sources of variability in language processing: speaker, text genre and experimental method.
The aim of this Research Topic is to develop a better understanding of prediction in light of the three sources of variability in language processing, by providing an overview of state-of-the art research on predictive language processing and by bringing together research from various disciplines.
First, intra-and inter-individual differences and their influence on predictive processes remain underrepresented in experimental research on predictive processing. How do language users differ in their predictive abilities and strategies, and how are these differences shaped by e.g. biological, social and cultural factors?
Second, while language users experience great stylistic diversity in their daily language exposure and use, the majority of language processing research still focuses on a very constrained register of well-controlled sentences composed in the standard language. How are predictions shaped by extra- and meta-linguistic context, such as register/genre or accent/speaker identity, and how may this influence the processing of experimental items in another language or text variety?
Third, the Research Topic invites contributions that make use of a multi-method approach, such as combined behavioral and electrophysiological measures or experimental methods combined with measures extracted from corpus data. What opportunities and challenges do we face when integrating multiple approaches to examine linguistic, experimental and individual differences in human predictive capacity?
We welcome contributions from all areas of empirical psycho- and neurolinguistics, but contributions must explicitly address variability and variation in language and language processing. Relevant topics include individual differences and the impact of genre, modality, register and language variety. Contributions that go beyond single word and single sentence paradigms are especially desirable. Experimental, corpus-based, meta-analytic and review papers, as well as theoretical/opinion pieces are welcome; however, papers of the latter type should support their arguments with substantial empirical evidence from the literature. Particularly desirable are contributions which combine topics and/or methods, such as the impact of an individual's native dialect on processing of constructions that show variability in the standard language (e.g. choice of auxiliary, agreement of mass nouns, etc.) or experimental methods combined with measures extracted from corpus data such as information-theoretic surprisal.
Simultandolmetschen ist eine komplexe und kognitive Aktivität, bei der verschiedene Prozesse gleichzeitig ablaufen. Neben monolingualer Textverarbeitung braucht man auch dolmetschspezifische Strategien, die erworben werden müssen. Die Notstrategien werden erst dann angewendet, wenn die Kapazitätsgrenze des Dolmetschers erreicht ist.
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.
Computational language models (LMs), most notably exemplified by the widespread success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range of linguistic tasks, thus providing cognitive science and linguistics with a computational working model to empirically study different aspects of human language. Here, we use LMs to test the hypothesis that languages with more speakers tend to be easier to learn. In two experiments, we train several LMs—ranging from very simple n-gram models to state-of-the-art deep neural networks—on written cross-linguistic corpus data covering 1293 different languages and statistically estimate learning difficulty. Using a variety of quantitative methods and machine learning techniques to account for phylogenetic relatedness and geographical proximity of languages, we show that there is robust evidence for a relationship between learning difficulty and speaker population size. However, contrary to expectations derived from previous research, our results suggest that languages with more speakers tend to be harder to learn.
Recent years have seen a growing interest in grammatical variation, a core explanandum of grammatical theory. The present volume explores questions that are fundamental to this line of research: First, the question of whether variation can always and completely be explained by intra- or extra-linguistic predictors, or whether there is a certain amount of unpredictable – or ‘free’ – grammatical variation. Second, the question of what implications the (in-)existence of free variation would hold for our theoretical models and the empirical study of grammar. The volume provides the first dedicated book-length treatment of this long-standing topic. Following an introductory chapter by the editors, it contains ten case studies on potentially free variation in morphology and syntax drawn from Germanic, Romance, Uralic and Mayan.
Allusion
(2023)
Assessment
(2023)
Most broadly, an assessment is a type of social action by which an interactant expresses an evaluative stance towards someone or something (e.g., an object, an event, an action, an experience, a state of affairs, a place, a circumstance, etc.). The target of an assessment is typically called the ‘assessable’.
Collaborative work in NFDI
(2023)
The non-profit association National Research Data Infrastructure (NFDI) promotes science and research through a National Research Data Infrastructure. Its aim is to develop and establish an overarching research data management (RDM) for Germany and to increase the efficiency of the entire German science system. After a two-and-a-half year build up phase, the process of adding new consortia, each representing a different data domain, has ended in March 2023. NFDI now has 26 disciplinary consortia (and one additional basic service collaboration). Now the full extent of cross-consortial interaction is beginning to show.
KoMuX, der Kompositamuster-Explorer, (www.owid.de/plus/komux) ist eine Webanwendung, die es ermöglicht, mehr als 50.000 nominale Komposita des Deutschen gezielt nach abstrakten oder lexikalisch-teilspezifizierten Mustern zu durchsuchen. Unterschiedliche Visualisierungen helfen dabei, Strukturen und Zusammenhänge innerhalb der Ergebnismenge zu erfassen.
Retro-sequence
(2023)
The Data Governance Act was proposed in late 2020 as part of the European Strategy for Data, and adopted on 30 May 2022 (as Regulation 2022/868). It will enter into application on 24 September 2023. The Data governance Act is a major development in the legal framework affecting CLARIN and the whole language community. With its new rules on the re-use of data held by the public sector bodies and on the provision of data sharing services, and especially its encouragement of data altruism, the Data Governance Act creates new opportunities and new challenges for CLARIN ERIC. This paper analyses the provisions of the Data Governance Act, and aims at initiating the debate on how they will impact CLARIN and the whole language community.
For many reasons, Mennonite Low German is a language whose documentation and investigation is of great importance for linguistics. To date, most research projects that deal with this language and/ or its speakers have had a relatively narrow focus, with many of the data cited being of limited relevance beyond the projects for which they were collected. In order to create a resource for a broad range of researchers, especially those working on Mennonite Low German, the dataset presented here has been transformed into a structured and searchable corpus that is accessible online. The translations of 46 English, Spanish, or Portuguese stimulus sentences into Mennonite Low German by 321 consultants form the core of the MEND-corpus (Mennonite Low German in North and South America) in the Archive for Spoken German. In addition to describing the origin of this corpus and discussing possibilities and limitations for further research, we discuss the technical structure and search possibilities of the Database for Spoken German. Among other things, this database allows for a structured search of metadata, a context-sensitive token search, and the generation of virtual corpora that can be shared with others. Moreover, thanks to its text-sound alignment, one can easily switch from a particular text section of the corpus to the corresponding audio section. Aside from the desire to equip the reader with the technical knowledge necessary to use this corpus, a further goal of this paper is to demonstrate that the corpus still offers many possibilities for future research.
Conventional terminology resources reach their limits when it comes to automatic content classification of texts in the domain of expertlayperson communication. This can be attributed to the fact that (non-normalized) language usage does not necessarily reflect the terminological elements stored in such resources. We present several strategies to extend a terminological resource with term-related elements in order to optimize automatic content classification of expert-layperson texts.
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.
Linguistische Studien arbeiten häufig mit einer Differenzierung zwischen gesprochener und geschriebener Sprache bzw. zwischen Kommunikation der Nähe und Distanz. Die Annahme eines Kontinuums zwischen diesen Polen bietet sich für eine Verortung unterschiedlichster Äußerungsformen an, inklusive unkonventioneller Textsorten wie etwa Popsongs. Wir konzipieren, implementieren und evaluieren ein automatisiertes Verfahren, das mithilfe unkorrelierter Entscheidungsbäume entsprechende Vorhersagen auf Textebene durchführt. Für die Identifizierung der Pole definieren wir einen Merkmalskatalog aus Sprachphänomenen, die als Markierer für Nähe/Mündlichkeit bzw. Distanz/Schriftlichkeit diskutiert werden, und wenden diesen auf prototypische Nähe-/Mündlichkeitstexte sowie prototypische Distanz-/Schrifttexte an. Basierend auf der sehr guten Klassifikationsgüte verorten wir anschließend eine Reihe weiterer Textsorten mithilfe der trainierten Klassifikatoren. Dabei erscheinen Popsongs als „mittige Textsorte“, die linguistisch motivierte Merkmale unterschiedlicher Kontinuumsstufen vereint. Weiterhin weisen wir nach, dass unsere Modelle mündlich kommunizierte, aber vorab oder nachträglich verschriftlichte Äußerungen wie Reden oder Interviews vollkommen anders verorten als prototypische Gesprächsdaten und decken Klassifikationsunterschiede für Social-Media-Varianten auf. Ziel ist dabei nicht eine systematisch-verbindliche Einordung im Kontinuum, sondern eine empirische Annäherung an die Frage, welche maschinell vergleichsweise einfach bestimmbaren Merkmale („shallow features“) nachweisbar Einfluss auf die Verortung haben.