Review Paper On Web-Base Question Answering system By Using Credibility Assessment Algorithm On The Basis Of Various categories For Improving Accuracy
Keywords:
Credibility assessment, information processing, Natural language processing, web credibilityAbstract
Web-based question answering (QA) systems are effective in corroborating answers from multiple Web sources. However, Web also contains false, fabricated, and biased information that can have adverse effects on the accuracy of answers in Web-based QA systems. Existing, solutions focus primarily on finding relevant Web pages but either do not evaluate Web pages’ credibility or evaluate two to three out of seven credibility categories. This research proposed a credibility assessment algorithm that uses seven categories, including correctness, authority, currency, professionalism, popularity, impartiality, quality, for scoring credibility, where each credibility category consists of multiple factors. The credibility assessment module is added on top of an existing QA system to score answers .
References
L. Hirschman and R. Gaizauskas, ‘‘Natural language question answering: The view from here,’’ Natural Lang. Eng., vol. 7, no. 4, pp. 275–300, Dec. 2001.
A. McCallum, ‘‘Information e xtraction: Distilling structured data from unstructured text,’’ Queue, vol. 3, no. 9, pp. 48–57, Nov. 2005.
D. Mamgai, S. Brodiya, R. Yadav, and M. Dua, ‘‘An improved automated question answering system from lecture videos,’’ in Proc. 2nd Int. Conf. Commun., Comput. Netw., vol. 2019, pp. 653–659.
P. Gupta and V. Gupta, ‘‘A survey of te xt question answering techniques,’’ Int. J. Comput. Appl., vol. 53, no. 4, pp. 1–8, Sep. 2012.
M. Devi and M. Dua, ‘‘ADANS: An agriculture domain question answering system using ontologies,’’ in Proc. Int. Conf. Comput., Commun. Autom. (ICCCA), May 2017, pp. 122–127.
K. Purcell, ‘‘Search and email still top the list of most popular online activities,’’ in Pew Internet & American Life Project, vol. 9. Washington, DC, USA: Pew Research Center’s Internet & American Life Project, 2011. [Online]. Available:
https://www.pewresearch.org/internet/wpcontent/uploads/si tes/ 9/m edia/Files/Reports/2011/PIP_Search-andEmail.pdf
M. Wu and A. Marian, ‘‘A framework for corroborating answers from multiple Web sources,’’ Inf. Syst., vol. 36, no. 2, pp. 431–449, Apr. 2011.
A. Abbasi, F. M. Zahedi, and S. Kaza, ‘‘Detecting fake medical Web sites using recursive trust labeling,’’ ACM Trans. Inf. Syst., vol. 30, no. 4, pp. 1–36, Nov. 2012.
A. Abbasi, Z. Zhang, D. Zimbra, H. Chen, and J.
F. Nunamaker, ‘‘Detecting fake Websites: The contribution of statistical learning theory,’’ Mis Quart., vol. 34, no. 3, pp. 435– 461, 2010.
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