“When researchers evaluated their model against natural language processing tools, such as Google’s SynaxNet (‘an open-source neural network framework’), researchers found that the software flagged African-American English as 'not English’ at a much higher frequency than standard English. In Twitter’s own language identifier, identification based on African-American language was twice as bad, despite the large presence of African-American users on the site.
'The standard tools being developed work worse on dialectical English,’ O’Connor says. 'Google’s parsers are going to do a worse job of analyzing it so it could be a case that our search systems might be worst at finding information from African-Americans. Language identification is the problem of you have a document and want language. This is a really crucial task. Your search engine only shows results written in English.’”
“Confidentially speakin’ code since I sense you peekin’” - Jay Z
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