Algorithmic Quality Control for Wikipedia
September 8, 2014
Wikipedia is a wonderful thing, but we all know it has its limitations. ScienceDaily points us to a tool that can help seekers of information separate the wheat from the chaff in, “Computer Program Assesses Quality of Wikipedia Entries.” The quality control system is the creation of computer scientists Jingyu Han and Kejia Chen at China’s Nanjing University of Posts and Telecommunications. The article reveals:
“Han and Chen have turned to Bayesian statistics to help them create just such a system…. Today, Bayesian analysis is commonly used to assess the content of emails and to determine the probability that the content is spam, junk mail, and so filter it from the user’s inbox if the probability is high.
“Han and Chen have now used dynamic Bayesian network (DBN) to analyze in a similar manner the content of Wikipedia entries. They apply multivariate Gaussian distribution modeling to the DBN analysis, which gives them a distribution of the quality of each article so that entries might be ranked. Very low-ranking entries might be flagged for editorial attention to raise the quality. By contrast, high-ranking entries could be marked in some way as the definitive entry so that such an entry is not subsequently overwritten with lower quality information.”
Reportedly, tests of the algorithm’s article assessment chops have performed well. In fact, they claim it out-performed human quality-rankers by 23 percent (though how they determined that is left unexplained). The article notes that an algorithmic quality control would “avoid the subjective need to have people classify each entry.” Wait, isn’t some subjectivity necessary? As far as I know, we have not developed an AI that can comprehend all the subtleties of human expression. Not yet, anyway.
Cynthia Murrell, September 08, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
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Algorithmic Quality Control for Wikipedia : Stephen E. Arnold @ Beyond Search