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We present a method for detecting and reconstructing separated particle verbs in a corpus of spoken German by following an approach suggested for written language. Our study shows that the method can be applied successfully to spoken language, compares different ways of dealing with structures that are specific to spoken language corpora, analyses some remaining problems, and discusses ways of optimising precision or recall for the method. The outlook sketches some possibilities for further work in related areas.
The CLARIN infrastructure as an interoperable language technology platform for SSH and beyond
(2023)
CLARIN is a European Research Infrastructure Consortium developing and providing a federated and interoperable platform to support scientists in the field of the Social Sciences and Humanities in carrying-out language-related research. This contribution provides an overview of the entire infrastructure with a particular focus on tool interoperability, ease of access to research data, tools and services, the importance of sharing knowledge within and across (national) communities, and community building. By taking into account FAIR principles from the very beginning, CLARIN succeeded in becoming a successful example of a research infrastructure that is actively used by its members. The benefits CLARIN members reap from their infrastructure secure a future for their common good that is both sustainable and attractive to partners beyond the original target groups.
Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution.We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity—the beta-MPT approach (Smith & Batchelder, Journal of Mathematical Psychology 54:167-183, 2010) and the latent-trait MPT approach (Klauer, Psychometrika 75:70-98, 2010). TreeBUGS reads standard MPT model files and obtains Markov-chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within- and between-subjects comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent-trait MPT model.
In two eye-tracking experiments, we investigated the relationship between the subject preference in the resolution of subject-object ambiguities in German embedded clauses and semantic word order constraints (i.e., prominence hierarchies relating to the specificity/referentiality of noun phrases, case assignment and thematic role assignment). Our central research question concerned the timecourse with which prominence information is used and particularly whether it modulates the subject preference. In both experiments, we replicated previous findings of reanalysis effects for object-initial structures. Our findings further suggest that noun phrase prominence does not alter initial parsing strategies (viz., the subject preference), but rather modulates the ease of later reanalysis processes. In Experiment 1, the object case assigned by the verb did not affect the ease of reanalysis. However, the syntactic reanalysis was rendered more difficult when the order of the two arguments violated the specificity/referentiality hierarchy. Experiment 2 revealed that the initial subject preference also holds for verbs favoring an object-initial base order (i.e., dative object-experiencer verbs). However, the advantage for subject-initial sentences is neutralized in relatively late processing stages when the thematic role hierarchy and the specificity hierarchy converge to promote scrambling.
This contribution explores the relationship between the English CEFR (Common European Framework of Reference for Languages) vocabulary levels and user interest in English Wiktionary entries. User interest was operationalized through the number of views of these entries in Wikimedia server logs covering a period of four years (2019–2022). Our findings reveal a significant relationship between CEFR levels and user interest: entries classified at lower CEFR levels tend to attract more views, which suggests a greater user interest in more basic vocabulary. A multiple regression model controlling for other known or potential factors affecting interest: corpus frequency, polysemy, word prevalence, and age of acquisition confirmed that lower CEFR levels attract significantly more views even after taking into account the other predictors. These findings highlight the importance of CEFR levels in predicting which words users are likely to look up, with implications for lexicography and the development of language learning materials.
Preface
(2015)
Russia, its languages and its ethnic groups are for many readers of English surprisingly unknown territory. Even among academics and researchers familiar with many ethnolinguistic situations around the globe, there prevails rather unsystematic and fragmented knowledge about Russia. This relates to both the micro level such as the individual situations of specific ethnic or linguistic groups, and to the macro level with regard to the entire interplay of linguistic practices, ideologies, laws, and other policies in Russia. In total, this lack of information about Russia stands in sharp contrast to the abundance of literature on ethnolinguistic situations, minority languages, language revitalization, and ideologies toward languages and multilingualism which has been published throughout the past decades.
Theater rehearsals are (usually) confronted with the problem of having to transform a written text into an audio-visual, situated and temporal performance. Our contribution focuses on the emergence and stabilization of a gestural form as a solution for embodying a certain aesthetic concept which is derived from the script. This process involves instructions and negotiations, making the process of stabilization publicly and thus intersubjectively accessible. As scenes are repeatedly rehearsed, rehearsals are perspicuous settings for tracking interactional histories. Based on videotaped professional theatre interactions in Germany, we focus on consecutive instances of rehearsing the same scene and trace the interactional history of a particular gesture. This gesture is used by the director to instruct the actors to play a particular aspect of a scene adopting a certain aesthetic concept. Stabilization requires the emergence of shared knowledge. We will show the practices by which shared knowledge is established over time during the rehearsal process and, in turn, how the accumulation of knowledge contributes to a change in the interactional practices themselves. Specifically, we show how a gesture emerges in the process of developing and embodying an aesthetic concept, and how this gesture eventually becomes a sign that refers to and evokes accumulated knowledge. At the same time, we show how this accumulated knowledge changes the instructional activities in the rehearsal process. Our study contributes to the overall understanding of knowledge accumulation in interaction in general and in theater rehearsals in particular. At the same time, it is devoted to the central importance of gestures in theater, which are both a means and a product of theatrical staging.
In the last years a common notion of a Problem-Solving Method (PSM) emerged from different knowledge engineering frameworks. As a generic description of the dynamic behaviour of knowledge based systems PSMs are favored subjects of reuse. Up to now, most investigations on the reuse of PSMs focus on static features and methods as objects of reuse. By this, they ignore a lot of information of how the PSM was developed that is, in principle, entailed in the different parts of a conceptual model of a PSM.
In this paper the information of the different parts of PSMs is reconsidered from a reuse process point of view. A framework for generalized problem-solving methods is presented that describes the structure of a category of methods based on family resemblances. These generalized methods can be used to structure libraries of PSMs and - in the process of reuse - as a means to derive an incarnation, i.e. a member of its family of PSMs.
For illustrating the ideas, the approach is applied to the task rsp. problem type of parametric design.
A library of software components should be essentially more than just a juxtaposition of its items. For problem-solving methods the notion of a family is suggested as means to cluster the items and to provide partially a structure of the library. This paper especially investigates how the similar control flows of the members of such a family can be described in one framework.
Corpora with high-quality linguistic annotations are an essential component in many NLP applications and a valuable resource for linguistic research. For obtaining these annotations, a large amount of manual effort is needed, making the creation of these resources time-consuming and costly. One attempt to speed up the annotation process is to use supervised machine-learning systems to automatically assign (possibly erroneous) labels to the data and ask human annotators to correct them where necessary. However, it is not clear to what extent these automatic pre-annotations are successful in reducing human annotation effort, and what impact they have on the quality of the resulting resource. In this article, we present the results of an experiment in which we assess the usefulness of partial semi-automatic annotation for frame labeling. We investigate the impact of automatic pre-annotation of differing quality on annotation time, consistency and accuracy. While we found no conclusive evidence that it can speed up human annotation, we found that automatic pre-annotation does increase its overall quality.