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Suchtipp für den Bereich Mehr: Wörter werden automatisch mit UND verknüpft. Eine ODER-Verknüpfung erreicht man mit dem Zeichen "|", eine NICHT-Verknüpfung mit einem "-" (Minus) vor einem Wort. Anführungszeichen ermöglichen eine Phrasensuche.
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  1. In: Sociologia Ruralis, Jg. 48 (2008-10-01), Heft 4, S. 423-426
    Online academicJournal
  2. Farrant, Andrew
    In: American Journal of Economics & Sociology, Jg. 67 (2008-07-01), Heft 3, S. 415-428
    Online academicJournal
    Siehe Detailanzeige für Volltext
  3. In: Journal of the British Grassland Society, Jg. 27 (1972-09-01), Heft 3, S. 195-202
    Online academicJournal
  4. class evaluation that classifies each statement as true, viewpoint or erroneous; and 2) 2-class evaluation that distinguishes between the true statements and all the others (i.e. viewpoint and erroneous statements were considered as one category). Interestingly, as shown in Table 2 the obtained results for the baselines are comparable for the two experiments, while for all the aggregation measures the second experiment's results were consistently higher (by up to 17%) than those of the first experiment. Therefore, we conclude that workers have quite a good ability to objectively assess the others' opinions, while their own opinions seem less reliable and consequently yield lower accuracy in classification. The accuracy of the individual worker judgment baseline is quite low for both experiments (0.7 and 0.72). Approximately 30,000 individual worker judgments were produced in each of the crowdsourcing experiments. Thus, every worker in isolation does not do any better than the "all true" baseline strategy (0.73), as could be expected. However, the workers' collective decisions (after aggregation) for each statement were much more accurate. We observe that the aggregation measure has a crucial influence on the results: a better aggregation measure can increase the accuracy by over 25% compared to the baselines. The best results were obtained by the Bayesian inference measure with alpha=0.5 and beta=0.5 for Jeffrey's prior. The AUC values are presented in Table 2 and the ROC curves are shown in Figure 3. This measure elicited 0.92 accuracy (by definition this measure could only be applied for the 2-class classification). CONCLUSION The main contribution of this research is that we show that crowdsourcing workers can quite accurately assess statements in a multi-viewpoint ontology to distinguish between true, viewpoint and erroneous statements for a given professional domain, and especially to differentiate true statements from the others. In addition, we found that a h
    Fenlon, Katrina
    In: Proceedings of the Association for Information Science & Technology, Jg. 52 (2015), Heft 1, S. 1-4
    Online Konferenz
  5. Brown, Jac
    In: Australian & New Zealand Journal of Family Therapy, Jg. 19 (1998-09-01), Heft 3, S. iv
    Online academicJournal
  6. In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 818-820
    Online Konferenz
  7. Md. Anwarul, Islam ; Sultana, Rafia ; et al.
    In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 715-717
    Online Konferenz
  8. Larner, Glenn
    In: Australian & New Zealand Journal of Family Therapy, Jg. 19 (1998-12-01), Heft 4, S. ii
    Online academicJournal
  9. Tsunoda, Hiroyuki ; Sun, Yuan ; et al.
    In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 505-509
    Online Konferenz
  10. Lee, Jian‐Sin ; Jeng, Wei
    In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 447-452
    Online Konferenz
  11. VanScoy, Amy ; Julien, Heidi ; et al.
    In: Proceedings of the Association for Information Science & Technology, Jg. 57 (2020-10-01), Heft 1, S. 1-3
    Online Konferenz
  12. Kim, Jeonghyun ; Faulkner, James
    In: Proceedings of the Association for Information Science & Technology, Jg. 58 (2021-10-01), Heft 1, S. 750-752
    Online Konferenz
  13. In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 779-781
    Online Konferenz
  14. Kumari, Madhuri ; Gala, Bhakti
    In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 732-734
    Online Konferenz
  15. Han, Yingying ; Maganti, Roopesh ; et al.
    In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 698-700
    Online Konferenz
  16. Yun‐Chi, Chang ; Li‐Fei, Kung ; et al.
    In: Proceedings of the Association for Information Science & Technology, Jg. 59 (2022-10-01), Heft 1, S. 636-638
    Online Konferenz
  17. Erdt, Moijsola ; Aw, Ashley Sara ; et al.
    In: Proceedings of the Association for Information Science & Technology, Jg. 53 (2016-10-01), Heft 1, S. 1-9
    Online Konferenz
  18. Islam, Md Anwarul ; Agarwal, Naresh Kumar
    In: Proceedings of the Association for Information Science & Technology, Jg. 56 (2019), Heft 1, S. 674-676
    Online Konferenz
  19. Ma, Yuanye ; Flaherty, Mary Grace
    In: Proceedings of the Association for Information Science & Technology, Jg. 58 (2021-10-01), Heft 1, S. 273-281
    Online Konferenz
  20. Zhang, Yin ; Chen, Hsin‐liang
    In: Proceedings of the Association for Information Science & Technology, Jg. 52 (2015), Heft 1, S. 1-4
    Online Konferenz

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