DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5
Hits 1 – 20 of 82

1
Domain-Adversarial Based Model with Phonological Knowledge for Cross-Lingual Speech Recognition
In: http://infoscience.epfl.ch/record/291292 (2022)
BASE
Show details
2
Online Literacy Instruction for Young Korean Dual Language Learners in General Education
In: J Behav Educ (2022)
BASE
Show details
3
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
BASE
Show details
4
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
BASE
Show details
5
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
BASE
Show details
6
From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network ...
BASE
Show details
7
Temporal trends in incidence and mortality rates of laryngeal cancer at the global, regional and national levels, 1990–2017
In: BMJ Open (2021)
BASE
Show details
8
Development and Validation of an Unethical Professional Behavior Tendencies Scale for Student Teachers
In: Front Psychol (2021)
BASE
Show details
9
Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study
In: J Med Internet Res (2021)
BASE
Show details
10
The second language acquisition of the Chinese aspect marker "le"
Wang, Jing. - : University of Kansas, 2021
BASE
Show details
11
Measurement of single-diffractive dijet production in proton-proton collisions at $\sqrt{s} =$ 8 TeV with the CMS and TOTEM experiments
In: Eur.Phys.J.C ; https://hal.archives-ouvertes.fr/hal-02507664 ; Eur.Phys.J.C, 2020, 80 (12), pp.1164. ⟨10.1140/epjc/s10052-020-08562-y⟩ (2020)
BASE
Show details
12
Lens on China: Intermediate and Advanced Readings on Film for Learning Chinese
In: Faculty Books (2020)
BASE
Show details
13
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
BASE
Show details
14
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
BASE
Show details
15
Neural representations of the concepts in simple sentences: Concept activation prediction and context effects ...
Just, Marcel; Wang, Jing; Cherkassky, Vladimir. - : Carnegie Mellon University, 2020
BASE
Show details
16
Neural representations of the concepts in simple sentences: Concept activation prediction and context effects ...
Just, Marcel; Wang, Jing; Cherkassky, Vladimir. - : Carnegie Mellon University, 2020
BASE
Show details
17
The role of growth mindset, self-efficacy and intrinsic value in self-regulated learning and English language learning achievements ...
Bai, Barry; Wang, Jing. - : SAGE Journals, 2020
BASE
Show details
18
The role of growth mindset, self-efficacy and intrinsic value in self-regulated learning and English language learning achievements ...
Bai, Barry; Wang, Jing. - : SAGE Journals, 2020
BASE
Show details
19
Measurement of the top quark mass with lepton+jets final states using $\mathrm {p}$ $\mathrm {p}$ collisions at $\sqrt{s}=13\,\text {TeV} $
In: http://infoscience.epfl.ch/record/275278 (2020)
BASE
Show details
20
Conversational topics of social media messages associated with state-level mental distress rates
In: J Ment Health (2020)
Abstract: BACKGROUND: Upstream public health indicators of poor mental health in the United States (U.S.) are currently measured by national telephone-based surveys; however, results are delayed by 1–2 years, limiting real-time assessment of trends. AIM: The aim of this study was to evaluate associations between conversational topics on Twitter from 2018 to 2019 and mental distress rates from 2017 to 2018 for the 50 U.S. states and capital. METHOD: We used a novel lexicon, Empath, to examine conversational topics from aggregate social media messages from Twitter that correlated most strongly with official U.S. state-level rates of mental distress from the Behavioral Risk Factor Surveillance System. RESULTS: The ten lexical categories most positively correlated with rates of frequent mental distress at the state-level included categories about death, illness, or injury. Lexical categories most inversely correlated with mental distress included categories that serve as proxies for economic prosperity and industry. Using the prevalence of the 10 most positively and 10 most negatively correlated lexical categories to predict state-level rates of mental distress via a linear regression model on an independent sample of data yielded estimates that were moderately similar to actual rates (mean difference = 0.52%; Pearson correlation = 0.45, p < 0.001). CONCLUSION: This work informs efforts to use social media to measure population-level trends in mental health.
Keyword: Article
URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217347/
http://www.ncbi.nlm.nih.gov/pubmed/32223489
https://doi.org/10.1080/09638237.2020.1739251
BASE
Hide details

Page: 1 2 3 4 5

Catalogues
0
0
8
0
2
0
0
Bibliographies
2
0
0
0
0
0
0
0
1
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
70
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern