We study the problem of disambiguating references to named people in web data. Each name spotted online is shared by several hundred people on average, and teasing apart these references is critical for a new family of person-aware analytical applications. We present and evaluate algorithms for this problem, and give results to indicate that 25% of personal references may be successfully disambiguated with precision in excess of 95%, but that larger fractions cause a significant decline in precision..
By: Levon Lloyd; Varun Bhagwan; Daniel F. Gruhl; Andrew Tomkins
Published in: RJ10364 in 2005
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