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1
Konrad von Würzburg : Ein Handbuch
Stock, Markus. - Berlin : De Gruyter, 2022
DNB Subject Category Language
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2
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice ...
BASE
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3
Sozio-oekonomisches Panel, Daten der Jahre 1984-2020 (SOEP-Core, v37, Teaching Version) ... : Socio-Economic Panel, data from 1984-2020, (SOEP-Core, v37, Teaching Version) ...
Liebig, Stefan; Goebel, Jan; Grabka, Markus. - : SOEP Socio-Economic Panel Study, 2022
BASE
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4
Sozio-oekonomisches Panel, Daten der Jahre 1984-2020 (SOEP-Core, v37, Add-on: Area Types) ... : Socio-Economic Panel, data from 1984-2020, (SOEP-Core, v37, Add-on: Area Types) ...
Liebig, Stefan; Goebel, Jan; Grabka, Markus. - : SOEP Socio-Economic Panel Study, 2022
BASE
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5
Sozio-oekonomisches Panel, Daten der Jahre 1984-2020 (SOEP-Core, v37, Onsite Edition) ... : Socio-Economic Panel, data from 1984-2020, (SOEP-Core, v37, Onsite Edition) ...
Liebig, Stefan; Goebel, Jan; Grabka, Markus. - : SOEP Socio-Economic Panel Study, 2022
BASE
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6
Sozio-oekonomisches Panel, Daten der Jahre 1984-2020 (SOEP-Core, v37, Add-on: Planning regions) ... : Socio-Economic Panel, data from 1984-2020, (SOEP-Core, v37, Add-on: Planning regions) ...
Liebig, Stefan; Goebel, Jan; Grabka, Markus. - : SOEP Socio-Economic Panel Study, 2022
BASE
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7
Sozio-oekonomisches Panel, Daten der Jahre 1984-2020 (SOEP-Core, v37, EU Edition) ... : Socio-Economic Panel, data from 1984-2020, (SOEP-Core, v37, EU Edition) ...
Liebig, Stefan; Goebel, Jan; Grabka, Markus. - : SOEP Socio-Economic Panel Study, 2022
BASE
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8
Sozio-oekonomisches Panel, Daten der Jahre 1984-2020 (SOEP-Core, v37, International Edition) ... : Socio-Economic Panel, data from 1984-2020, (SOEP-Core, v37, International Edition) ...
Liebig, Stefan; Goebel, Jan; Grabka, Markus. - : SOEP Socio-Economic Panel Study, 2022
BASE
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9
Sozio-oekonomisches Panel, Daten der Jahre 1984-2020 (SOEP-Core, v37, Remote Edition) ... : Socio-Economic Panel, data from 1984-2020, (SOEP-Core, v37, Remote Edition) ...
Liebig, Stefan; Goebel, Jan; Grabka, Markus. - : SOEP Socio-Economic Panel Study, 2022
BASE
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10
The Demography of COVID-19 Deaths Database: A gateway to well-documented international data ...
BASE
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11
The Demography of COVID-19 Deaths Database: A gateway to well-documented international data ...
BASE
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12
Schizotypy and Theory of Mind in a second language: Evidence from German-English bilinguals ...
Samuel, Steven; Boeckle, Markus. - : Unpublished, 2022
BASE
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13
Color Term Knowledge Across the Lifespan
Christina Bergmann; Guillermo Montero-Melis; Lena Ackermann. - : The Language Archive, Max Planck Institute for Psycholinguistics, 2022
BASE
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14
Behavioural data and lab notes
Eva Poort; Markus Ostarek. - : The Language Archive, Max Planck Institute for Psycholinguistics, 2022
BASE
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15
A multi-lab replication of Nozaradan et al. (2011) – Lab huettig-mpi
Eva Poort; Markus Ostarek. - : The Language Archive, Max Planck Institute for Psycholinguistics, 2022
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16
Data
Christina Bergmann; Guillermo Montero-Melis; Lena Ackermann. - : The Language Archive, Max Planck Institute for Psycholinguistics, 2022
BASE
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17
coqui-ai/TTS: v0.5.0 ...
BASE
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18
Language Models Explain Word Reading Times Better Than Empirical Predictability ...
Abstract: Though there is a strong consensus that word length and frequency are the most important single-word features determining visual-orthographic access to the mental lexicon, there is less agreement as how to best capture syntactic and semantic factors. The traditional approach in cognitive reading research assumes that word predictability from sentence context is best captured by cloze completion probability (CCP) derived from human performance data. We review recent research suggesting that probabilistic language models provide deeper explanations for syntactic and semantic effects than CCP. Then we compare CCP with (1) Symbolic n-gram models consolidate syntactic and semantic short-range relations by computing the probability of a word to occur, given two preceding words. (2) Topic models rely on subsymbolic representations to capture long-range semantic similarity by word co-occurrence counts in documents. (3) In recurrent neural networks (RNNs), the subsymbolic units are trained to predict the next word, ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2202.01128
https://arxiv.org/abs/2202.01128
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19
Individual Perception of Telehealth: Validation of a German Translation of the Telemedicine Perception Questionnaire and a Derived Short Version
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 2; Pages: 902 (2022)
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20
Individual Differences in Singing Behavior during Childhood Predicts Language Performance during Adulthood
In: Languages; Volume 7; Issue 2; Pages: 72 (2022)
BASE
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