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Although the N400 was originally discovered in a paradigm designed to elicit a P300 (Kutas and Hillyard, 1980), its relationship with the P300 and how both overlapping event-related potentials (ERPs) determine behavioral profiles is still elusive. Here we conducted an ERP (N = 20) and a multiple-response speed-accuracy tradeoff (SAT) experiment (N = 16) on distinct participant samples using an antonym paradigm (The opposite of black is white/nice/yellow with acceptability judgment). We hypothesized that SAT profiles incorporate processes of task-related decision-making (P300) and stimulus-related expectation violation (N400). We replicated previous ERP results (Roehm et al., 2007): in the correct condition (white), the expected target elicits a P300, while both expectation violations engender an N400 [reduced for related (yellow) vs. unrelated targets (nice)]. Using multivariate Bayesian mixed-effects models, we modeled the P300 and N400 responses simultaneously and found that correlation between residuals and subject-level random effects of each response window was minimal, suggesting that the components are largely independent. For the SAT data, we found that antonyms and unrelated targets had a similar slope (rate of increase in accuracy over time) and an asymptote at ceiling, while related targets showed both a lower slope and a lower asymptote, reaching only approximately 80% accuracy. Using a GLMM-based approach (Davidson and Martin, 2013), we modeled these dynamics using response time and condition as predictors. Replacing the predictor for condition with the averaged P300 and N400 amplitudes from the ERP experiment, we achieved identical model performance. We then examined the piecewise contribution of the P300 and N400 amplitudes with partial effects (see Hohenstein and Kliegl, 2015). Unsurprisingly, the P300 amplitude was the strongest contributor to the SAT-curve in the antonym condition and the N400 was the strongest contributor in the unrelated condition. In brief, this is the first demonstration of how overlapping ERP responses in one sample of participants predict behavioral SAT profiles of another sample. The P300 and N400 reflect two independent but interacting processes and the competition between these processes is reflected differently in behavioral parameters of speed and accuracy.
In this paper we examine the composition and interactional deployment of suspended assessments in ordinary German conversation. We define suspended assessments as lexicosyntactically incomplete assessing TCUs that share a distinct cluster of prosodic-phonetic features which auditorily makes them come off as 'left hanging' rather than cut-off (e.g., Schegloff/Jefferson/Sacks 1977; Jasperson 2002) or trailing-off (e.g., Local/Kelly 1986; Walker 2012). Using CA/IL methodology (Couper-Kuhlen/Selting 2018) and drawing on a large body of video-recorded face-to-face conversations, we highlight the verbal, vocal and bodily-visual resources participants use to render such unfinished assessing TCUs recognizably incomplete and identify six recurrent usage types. Overall, the suspension of assessing TCUs appears to either serve as a practice for circumventing the production of assessments that are interactionally inapposite, or as a practice for coping with local contingencies that render the very doing of an assessment problematic for the speaker. Data are in German with English translations.
Preface
(2019)
Preface
(2020)
Physicists look at language
(2006)
This paper aims at verifying if the most important online Brazilian Portuguese dictionaries include some of the neologisms identified in texts published in the 1990s to 2000s, formed with the elements ciber-, e-, bio-, eco- and narco, which we refer to as fractomorphemes / fracto-morphèmes. Three online dictionaries were analyzed (Aulete, Houaiss and Michaelis), as well as Vocabulário Ortográfico da Língua Portuguesa (VOLP). We were able to conclude that all three dictionaries and VOLP include neologisms with these elements; Michaelis and VOLP do not include separate entries for bound morphemes, whereas Houaiss includes entries for all of them and Aulete includes entries for bio-, eco- and narco-. Aulete also describes the neological meaning of eco- and narco-, whereas Houaiss does not.
This White Paper sets out commonly agreed definitions on activities of consortia within NFDI. It aims to provide a common basis for reporting and reference regarding selected questions of cross-consortial relevance in DFG’s template for the Interim Reports. The questions were prioritised by an NFDI Task Force on Evaluation and Reporting (formerly Task Force Monitoring) as a result of discussing possible answers to the DFG template. In this process the need to agree on a generalizable meaning of terms commonly used in the context of NFDI, and reporting in particular, were identified from cross-consortial perspectives. Questions that showed the highest requirement on clarification are discussed in this White Paper. As NFDI evolves, the Task Force will likely propose further joint approaches for reporting in information infrastructures.
While each of broad relevance, the questions addressed relate to substantially different aspects of consortia’s work. They are thus also structured slightly different.
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.
The automatic recognition of idioms poses a challenging problem for NLP applications. Whereas native speakers can intuitively handle multiword expressions whose compositional meanings are hard to trace back to individual word semantics, there is still ample scope for improvement regarding computational approaches. We assume that idiomatic constructions can be characterized by gradual intensities of semantic non-compositionality, formal fixedness, and unusual usage context, and introduce a number of measures for these characteristics, comprising count-based and predictive collocation measures together with measures of context (un)similarity. We evaluate our approach on a manually labelled gold standard, derived from a corpus of German pop lyrics. To this end, we apply a Random Forest classifier to analyze the individual contribution of features for automatically detecting idioms, and study the trade-off between recall and precision. Finally, we evaluate the classifier on an independent dataset of idioms extracted from a list of Wikipedia idioms, achieving state-of-the art accuracy.