Hasil untuk "German literature"

S. Holt, S. Schmiedl, P. Thürmann

B. Praag, A. Ferrer-i-Carbonell

Tanja A. Börzel

T. Flatten, A. Engelen, S. Zahra et al.

Jason Weismueller, P. Harrigan, Shasha Wang et al.

This paper investigates the impact of social media influencer endorsements on purchase intention, more specifically, the impact advertising disclosure and source credibility have in this process. The proposed framework argues that advertising disclosure has a significant impact on source credibility subdimensions of attractiveness, trustworthiness and expertise; subdimensions that positively influence consumer purchase intention. Empirical findings based on 306 German Instagram users between 18 and 34 years of age reveal that source attractiveness, source trustworthiness and source expertise significantly increase consumer purchase intention; whilst advertising disclosure indirectly influences consumer purchase intention by influencing source attractiveness. Furthermore, the results reveal that the number of followers positively influences source attractiveness, source trustworthiness as well as purchase intention. All in all, this paper makes a unique contribution to product endorsement literature, with evidence highlighting how social media influencers and advertising disclosure may be used on Instagram to effectively increase consumer purchase intention.

R. Pekrun, A. Elliot, Markus A. Maier

D. Eckstein, Matthias Goellner, C. Blome et al.

G. Baetschmann, K. E. Staub, R. Winkelmann

The paper considers panel data methods for estimating ordered logit models with individual‐specific correlated unobserved heterogeneity. We show that a popular approach is inconsistent, whereas some consistent and efficient estimators are available, including minimum distance and generalized method‐of‐moment estimators. A Monte Carlo study reveals the good properties of an alternative estimator that has not been considered in econometric applications before, is simple to implement and almost as efficient. An illustrative application based on data from the German Socio‐Economic Panel confirms the large negative effect of unemployment on life satisfaction that has been found in the previous literature.

Daniel Kiel, Christian Arnold, K. Voigt

Georg Keilbar, Sonja Greven

We propose a novel framework for conducting causal inference based on counterfactual densities. While the current paradigm of causal inference is mostly focused on estimating average treatment effects (ATEs), which restricts the analysis to the first moment of the outcome variable, our density-based approach is able to detect causal effects based on general distributional characteristics. Following the Oaxaca-Blinder decomposition approach, we consider two types of counterfactual density effects that together explain observed discrepancies between the densities of the treated and control group. First, the distribution effect is the counterfactual effect of changing the conditional density of the control group to that of the treatment group, while keeping the covariates fixed at the treatment group distribution. Second, the covariate effect represents the effect of a hypothetical change in the covariate distribution. Both effects have a causal interpretation under the classical unconfoundedness and overlap assumptions. Methodologically, our approach is based on analyzing the conditional densities as elements of a Bayes Hilbert space, which preserves the non-negativity and integration-to-one constraints. We specify a flexible functional additive regression model estimating the conditional densities. We apply our method to analyze the German East--West income gap, i.e., the observed differences in wages between East Germans and West Germans. While most of the existing studies focus on the average differences and neglect other distributional characteristics, our density-based approach is suited to detect all nuances of the counterfactual distributions, including differences in probability masses at zero.

Theresa Pekarek Rosin, Burak Can Kaplan, Stefan Wermter

Intent recognition (IR) for speech commands is essential for artificial intelligence (AI) assistant systems; however, most existing approaches are limited to short commands and are predominantly developed for English. This paper addresses these limitations by focusing on IR from speech by elderly German speakers. We propose a novel approach that combines an adapted Whisper ASR model, fine-tuned on elderly German speech (SVC-de), with Transformer-based language models trained on synthetic text datasets generated by three well-known large language models (LLMs): LeoLM, Llama3, and ChatGPT. To evaluate the robustness of our approach, we generate synthetic speech with a text-to-speech model and conduct extensive cross-dataset testing. Our results show that synthetic LLM-generated data significantly boosts classification performance and robustness to different speaking styles and unseen vocabulary. Notably, we find that LeoLM, a smaller, domain-specific 13B LLM, surpasses the much larger ChatGPT (175B) in dataset quality for German intent recognition. Our approach demonstrates that generative AI can effectively bridge data gaps in low-resource domains. We provide detailed documentation of our data generation and training process to ensure transparency and reproducibility.

Kristin Gnadt, David Thulke, Simone Kopeinik et al.

In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods initially developed for the English language when applied to other languages. This work aims to contribute to this research strand by presenting five German datasets for gender bias evaluation in LLMs. The datasets are grounded in well-established concepts of gender bias and are accessible through multiple methodologies. Our findings, reported for eight multilingual LLM models, reveal unique challenges associated with gender bias in German, including the ambiguous interpretation of male occupational terms and the influence of seemingly neutral nouns on gender perception. This work contributes to the understanding of gender bias in LLMs across languages and underscores the necessity for tailored evaluation frameworks.

Dinh Nam Pham, Torsten Rahne

When reading lips, many people benefit from additional visual information from the lip movements of the speaker, which is, however, very error prone. Algorithms for lip reading with artificial intelligence based on artificial neural networks significantly improve word recognition but are not available for the German language. A total of 1806 video clips with only one German-speaking person each were selected, split into word segments, and assigned to word classes using speech-recognition software. In 38,391 video segments with 32 speakers, 18 polysyllabic, visually distinguishable words were used to train and validate a neural network. The 3D Convolutional Neural Network and Gated Recurrent Units models and a combination of both models (GRUConv) were compared, as were different image sections and color spaces of the videos. The accuracy was determined in 5000 training epochs. Comparison of the color spaces did not reveal any relevant different correct classification rates in the range from 69% to 72%. With a cut to the lips, a significantly higher accuracy of 70% was achieved than when cut to the entire speaker's face (34%). With the GRUConv model, the maximum accuracies were 87% with known speakers and 63% in the validation with unknown speakers. The neural network for lip reading, which was first developed for the German language, shows a very high level of accuracy, comparable to English-language algorithms. It works with unknown speakers as well and can be generalized with more word classes.

Mamatov, Gleb Максимович

The article studies the theoretical basis of the concept of alpine text and its functioning in the poem of a representative of the first wave of the Russian émigré N. P. Gronsky “Belladonne”. This term, recently introduced into scientific usage, is studied on the basis of theoretical works by T. Scheidegger, L. Lyubimova, N. Mednis. The article introduces major principles that characterize alpine text in connection with the aesthetics of Swiss and German Art Nouveau, the mountainscape and mountain philosophy of the Russian literature. Features of alpine text are researched in the poem “Belladonne”, dedicated to the conquest of the peak of the same name in the Dauphine Alps. N. Gronsky follows both the traditions of European Art Nouveau and Russian classical literature. The conflict of the hero-conqueror and the element, and motives of death correlated with it, wild and pristine nature, danger and eroticism are characteristic for the alpine text of the world literature. At the same time religious motives and images are central, and the motive of a snowy dessert, often appearing in the mountain texts of the Russian classics, has connotations of spiritual loneliness, traditional for the poets of the Russian émigré in connection with the loss of their Motherland and forced residence in a “foreign” world. Religious symbolism connected with the images of the mountain Virgin Mother and granite Christ is studied. In these images the fusion of pagan and Christian principles, typical of Gronsky’s poetry, is manifested. Three parallel storylines are analyzed, correlating with the three heroes-alpinists who conquer a dangerous peak; the plot of the climbing is comprehended by the poet with the Christian themes of sin and retribution, purity of spiritual thoughts and finding happiness, religious integrity and salvation, chaos and harmony.

Eamon T. Campolettano, John M. Scanlon, Timothy L. McMurry et al.

Vision Zero represents a road safety approach with aspirations toward eliminating serious and fatal injuries associated with traffic collisions. Given the well-described relationship between speed at impact and injury outcomes, many researchers have used a variety of methodological approaches to develop speed thresholds associated with human injury tolerance levels for serious and fatal injuries. The goal of this study was to present a framework based on state-of-the-art injury risk models using the latest field data and featuring biomechanically-relevant predictors in order to create safe impact speed thresholds. Tolerance-based Assessment of Risk for Generalized Event Thresholds (TARGET) values for safe speeds for several sets of the most commonly observed collision geometries and partners were estimated using previously-developed injury risk models. Consistent with prior literature, an injury tolerance level of 10% risk at the MAIS3+ severity level was evaluated given its association with high severity injury outcomes. Leveraging models built on German collision data for VRUs, the safe impact speed thresholds were 34 kph for pedestrians and 49 kph for cyclists and motorcyclists. Using models built on U.S. collision data for collisions involving passenger vehicles, the thresholds for closing speed were 99 kph for a frontal collision, 73 kph for a near-side collision, and 126 kph for a rear-end collision. The TARGET values established in this study are consistent with those previously developed and can serve as a validation of these previous studies. As an additional demonstrative, we highlighted other factors (increased age and vehicle seating position) that affect serious and fatal injury risk and were associated with decreased safe impact speed thresholds. This study used a data-driven approach, injury risk models with additional biomechanically-relevant predictors, and the most modern collision data to provide a more precise approach to quantify generalized speed thresholds associated with biomechanical tolerance for humans involved in automotive collisions. Given the relationships between speed and injury risk, reducing speed in a collision below these thresholds is key to mitigating serious and fatal injury outcomes. The objective injury risk approach used in this study enables traffic safety practitioners to determine the relative effect of related safety countermeasures on reaching the goals of Vision Zero and a Safe System Approach.

Anna-Janina Stephan, Michael Hanselmann, Medina Bajramovic et al.

Abstract Background Digitalization and big health system data open new avenues for targeted prevention and treatment strategies. We aimed to develop and validate prediction models for stroke and myocardial infarction (MI) in patients with type 2 diabetes based on routinely collected high-dimensional health insurance claims and compared predictive performance of traditional regression with state-of-the-art machine learning including deep learning methods. Methods We used German health insurance claims from 2014 to 2019 with 287 potentially relevant literature-derived variables to predict 3-year risk of MI and stroke. Following a train-test split approach, we compared the performance of logistic methods with and without forward selection, LASSO-regularization, random forests (RF), gradient boosting (GB), multi-layer-perceptrons (MLP) and feature-tokenizer transformers (FTT). We assessed discrimination (Areas Under the Precision-Recall and Receiver-Operator Curves, AUPRC and AUROC) and calibration. Results Among n = 371,006 patients with type 2 diabetes (mean age: 67.2 years), 3.5% (n = 13,030) had MIs and 3.4% (n = 12,701) strokes. AUPRCs were 0.035 (MI) and 0.034 (stroke) for a null model, between 0.082 (MLP) and 0.092 (GB) for MI, and between 0.061 (MLP) and 0.073 (GB) for stoke. AUROCs were 0.5 for null models, between 0.70 (RF, MLP, FTT) and 0.71 (all other models) for MI, and between 0.66 (MLP) and 0.69 (GB) for stroke. All models were well calibrated. Conclusions Discrimination performance of claims-based models reached a ceiling at around 0.09 AUPRC and 0.7 AUROC. While for AUROC this performance was comparable to existing epidemiological models incorporating clinical information, comparison of other, potentially more relevant metrics, such as AUPRC, sensitivity and Positive Predictive Value was hampered by lack of reporting in the literature. The fact that machine learning including deep learning methods did not outperform more traditional approaches may suggest that feature richness and complexity were exploited before the choice of algorithm could become critical to maximize performance. Future research might focus on the impact of different feature derivation approaches on performance ceilings. In the absence of other more powerful screening alternatives, applying transparent regression-based models in routine claims, though certainly imperfect, remains a promising scalable low-cost approach for population-based cardiovascular risk prediction and stratification. Graphical abstract

Lars Klöser, Mika Beele, Jan-Niklas Schagen et al.

This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts. We demonstrate the effectiveness of our approach with real-world online texts. Addressing the challenge of data scarcity in language simplification, we crawled professionally simplified German texts and synthesized a corpus using GPT-4. We finetune Large Language Models with up to 13 billion parameters on this data and evaluate their performance. This paper employs various methodologies for evaluation and demonstrates the limitations of currently used rule-based metrics. Both automatic and manual evaluations reveal that our models can significantly simplify real-world online texts, indicating the potential of synthetic data in improving text simplification.

Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer et al.

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.

Nils Constantin Hellwig, Jakob Fehle, Markus Bink et al.

We present GERestaurant, a novel dataset consisting of 3,078 German language restaurant reviews manually annotated for Aspect-Based Sentiment Analysis (ABSA). All reviews were collected from Tripadvisor, covering a diverse selection of restaurants, including regional and international cuisine with various culinary styles. The annotations encompass both implicit and explicit aspects, including all aspect terms, their corresponding aspect categories, and the sentiments expressed towards them. Furthermore, we provide baseline scores for the four ABSA tasks Aspect Category Detection, Aspect Category Sentiment Analysis, End-to-End ABSA and Target Aspect Sentiment Detection as a reference point for future advances. The dataset fills a gap in German language resources and facilitates exploration of ABSA in the restaurant domain.

NICOLETTA FRANCOVICH ONESTI

As the title of a 2007 Magdeburg conference goes (Namen des Frühmittelalters als sprachliche Zeugnisse und als Geschichtsquellen), onomastics can be a special type of historical source, as well as an early witness of linguistic developments, especially for the early medieval centuries, when Romance languages were not yet recorded in written documents. Onomastics and Germanic words occurring in Italian written sources can therefore reflect early Romance phonetic developments that would not otherwise show at such early dates. This paper tries to collect all evidence from names and from Germanic words (Gothic Ghiveric, Lombard Arnucciolu, ischerpa, isnaidas etc.) that may reflect early Romance tendencies in a Latin context. Among them, in Tuscany as early as the 8th century we find traces of a local [ž] sound, developed from the corresponding voiceless consonant, the new Italian preposition da ‘from’ occurring before proper names, and also the new Romance suffixes occurring in anthroponyms. In general we can also grasp the beginnings of dialect characteristics, from northern Italian to Tuscan and to southern dialects.