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  • SAGE Publications  (4)
  • Linguistics  (4)
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  • SAGE Publications  (4)
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  • Linguistics  (4)
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  • 1
    Online Resource
    Online Resource
    How Different Types of Linguistic Information Impact Voice Perception: Evidence From the Language-Familiarity Effect
    SAGE Publications ; 2023
    In:  Language and Speech Vol. 66, No. 4 ( 2023-12), p. 1007-1029
    Details
    In: Language and Speech, SAGE Publications, Vol. 66, No. 4 ( 2023-12), p. 1007-1029
    Abstract: Previous studies have suggested the effect of linguistic information on voice perception (e.g., the language-familiarity effect [LFE]). However, it remains unclear which type of specific information in speech contributes to voice perception, including acoustic, phonological, lexical, and semantic information. It is also underexamined whether the roles of these different types of information are modulated by the experimental paradigm (speaker discrimination vs. speaker identification). In this study, we conducted two experiments to investigate these issues regarding LFEs. Experiment 1 examined the roles of acoustic and phonological information in speaker discrimination and identification with forward and time-reversed Mandarin and Indonesian sentences. Experiment 2 further identified the role s of phonological, lexical, and semantic information with forward, word-scrambled, and reconstructed (consisting of pseudo-Mandarin words) Mandarin and forward Indonesian sentences. For Mandarin-only participants, in Experiment 1, speaker discrimination was more accurate for forward than reversed sentences, but there was no LFE in either sentence. Speaker identification was also more accurate for forward than reversed sentences, whereas there was an LFE for forward sentences. In Experiment 2, speaker discrimination was better for word-scrambled than reconstructed Mandarin sentences. Speaker identification was more accurate for forward and word-scrambled Mandarin sentences but less accurate for Mandarin reconstructed and forward Indonesian sentences. In general, the pattern of the results for Indonesian learners was the same as that for Mandarin-only speakers. These results suggest that different kinds of information support speaker discrimination and identification in native and unfamiliar languages. The LFE in speaker identification depends on both phonological and lexical information.
    Type of Medium: Online Resource
    ISSN: 0023-8309 , 1756-6053
    URL: Article
    DOI: 10.1177/00238309221143062
    RVK:
    EQ 1000
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 3312-1
    detail.hit.zdb_id: 2001596-3
    SSG: 5,2
    SSG: 7,11
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Diagnostic Utility of the PTSD Checklist in Detecting PTSD in Chinese Earthquake Victims
    SAGE Publications ; 2010
    In:  Psychological Reports Vol. 107, No. 3 ( 2010-12), p. 733-739
    Details
    In: Psychological Reports, SAGE Publications, Vol. 107, No. 3 ( 2010-12), p. 733-739
    Abstract: By using the posttraumatic stress disorder (PTSD) module of the Mini International Neuropsychiatric Interview (MINI) as the gold standard to identify PTSD, the current study investigated the diagnostic efficiency of the PTSD Checklist (PCL) as a screening questionnaire in a sample of 152 Chinese victims of the Wenchuan earthquake. The results showed that the area under the receiver operating characteristic (ROC) curve was .96 ( SE = .02), and the optimally efficient cut-off score was 44. The findings provide preliminary support of the PCL as a screening questionnaire among Chinese earthquake victims. The present study may contribute to further PTSD-related research and practices in Chinese disaster settings.
    Type of Medium: Online Resource
    ISSN: 0033-2941 , 1558-691X
    URL: Article
    DOI: 10.2466/03.15.20.PR0.107.6.733-739
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2010
    detail.hit.zdb_id: 205658-6
    detail.hit.zdb_id: 2066930-6
    SSG: 5,2
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Symptoms of Posttraumatic Stress Disorder among Health Care Workers in Earthquake-Affected Areas in Southwest China
    SAGE Publications ; 2010
    In:  Psychological Reports Vol. 106, No. 2 ( 2010-04), p. 555-561
    Details
    In: Psychological Reports, SAGE Publications, Vol. 106, No. 2 ( 2010-04), p. 555-561
    Abstract: The symptoms of posttraumatic stress disorder and associated risk factors were investigated among health care workers in earthquake-affected areas in southwest China. 343 health care workers completed the Chinese version of the Impact of Event Scale–Revised 3 mo. after the Wenchuan Earthquake. The prevalence of probable PTSD was 19%. The significant risk factors identified for PTSD severity included being female, being bereaved, being injured, and higher intensity of initial fear. These findings suggest that PTSD is a common mental health problem among health care workers in earthquake-affected areas. The present information can be useful in directing, strengthening, and evaluating disaster-related mental health needs and interventions after an earthquake.
    Type of Medium: Online Resource
    ISSN: 0033-2941 , 1558-691X
    URL: Article
    DOI: 10.2466/pr0.106.2.555-561
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2010
    detail.hit.zdb_id: 205658-6
    detail.hit.zdb_id: 2066930-6
    SSG: 5,2
    Location Call Number Limitation Availability
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    Online Resource
  • 4
    Online Resource
    Online Resource
    A deep learning-based quality assessment model of collaboratively edited documents: A case study of Wikipedia
    SAGE Publications ; 2021
    In:  Journal of Information Science Vol. 47, No. 2 ( 2021-04), p. 176-191
    Details
    In: Journal of Information Science, SAGE Publications, Vol. 47, No. 2 ( 2021-04), p. 176-191
    Abstract: Wikipedia is becoming increasingly critical in helping people obtain information and knowledge. Its leading advantage is that users can not only access information but also modify it. However, this presents a challenging issue: how can we measure the quality of a Wikipedia article? The existing approaches assess Wikipedia quality by statistical models or traditional machine learning algorithms. However, their performance is not satisfactory. Moreover, most existing models fail to extract complete information from articles, which degrades the model’s performance. In this article, we first survey related works and summarise a comprehensive feature framework. Then, state-of-the-art deep learning models are introduced and applied to assess Wikipedia quality. Finally, a comparison among deep learning models and traditional machine learning models is conducted to validate the effectiveness of the proposed model. The models are compared extensively in terms of their training and classification performance. Moreover, the importance of each feature and the importance of different feature sets are analysed separately.
    Type of Medium: Online Resource
    ISSN: 0165-5515 , 1741-6485
    URL: Article
    DOI: 10.1177/0165551519877646
    RVK:
    AN 10100
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 439125-1
    detail.hit.zdb_id: 2025062-9
    SSG: 24,1
    Location Call Number Limitation Availability
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    Online Resource
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